The High Expression of p53 Is Predictive of Poor Survival Rather <i>TP53</i> Mutation in Esophageal Squamous Cell Carcinoma

Jin, Yan; Zhao, Xueke; Song, Xin; Wang, Ran; Fan, Zongmin; Wang, Panpan; Yang, Miaomiao; Zhou, Fuyou; Bao, Qide; Wang, Lidong

doi:https://doi.org/10.1155/2023/3801526

Journal of Oncology

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Abstract Introduction Materials and Methods Results Discussion Data Availability Ethical Approval Consent Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 3801526 | https://doi.org/10.1155/2023/3801526

The High Expression of p53 Is Predictive of Poor Survival Rather TP53 Mutation in Esophageal Squamous Cell Carcinoma

Yan Jin,^1,2Xueke Zhao,¹Xin Song,¹Ran Wang,¹Zongmin Fan,¹Panpan Wang,¹Miaomiao Yang,¹Fuyou Zhou,³Qide Bao,⁴and Lidong Wang¹

Academic Editor: Reza Izadpanah

Received10 Sept 2022

Revised11 Nov 2022

Accepted16 Nov 2022

Published10 Jan 2023

Abstract

TP53 is a well-known tumor suppressor gene and one of the most common genetic alterations in human cancers. However, the role of p53 as a prognostic marker of esophageal squamous cell carcinoma (ESCC) is controversial in the association between TP53 alterations and clinical outcomes. To address this issue, we evaluated TP53 mutations, p53 protein expression, clinicopathological parameters, and survivals rates in a large scale of patients with ESCC. Two cohorts were included in this study: TP53 mutations were detected by next-generation sequencing in 316 ESCC patients, and p53 protein expression was tested by immunohistochemistry in 6,028 ESCC patients. Survival analysis was performed using the Kaplan–Meier curve and the Cox proportional hazards model. TP53 mutations were found in ESCC patients from 241 of 316 (76.3%), and the rate of positive expression of p53 protein was 59.1% in 6,028 ESCC patients (including 1819 with high expression of p53 protein), respectively. Most mutations were missense, which has a high expression of p53 protein. Compared with wild-typeTP53, TP53 gene mutations were not significantly associated with survival time (). In multivariate analysis, the p53 protein expression was an independent prognostic factor for ESCC. The high-expression group of p53 protein has poor survival () compared to low-expression group in patients with ESCC. The high expression of the p53 protein, not the TP53 mutation, is predictive of poor survival in patients with ESCC, and p53 protein expression might have the potential to be a prognosis biomarker and therapy target in ESCC.

1. Introduction

Esophageal carcinoma is one of the most aggressive cancers and the sixth leading cause of cancer death [1]. Esophageal cancer (EC) is the fourth most common malignancy associated with cancer-related death, and esophageal squamous cell carcinoma (ESCC) is the most common pathological subtypes (>90%) in China [2–5]. With limited early clinical diagnosis approaches and few targeted therapies, although the five-year survival rate has improved during past decades, it still remains dismal, only at 10%–30% in most countries [6]. The most effective way to improve the survival rate of ESCC patients is through early detection and treatment. Therefore, the discovery of molecular markers for early screening, prognosis, and efficacy of evaluation to provide personalized treatment for ESCC patients is one of the entry points to reduce the incidence and improve the survival rate of patients with ESCC.

TP53 is one of the most frequently mutated genes in human cancers and has occurred in more than 50% of all human cancers [7, 8]. A recent study analyzed the TP53 mutational spectra of 7,525 pan-cancer tissues and found TP53 mutations in 35% of all 30 tumor-type samples, of which the most mutated cohorts are more than 80% and the lowest percentage of TP53 mutation was less than 1% [9]. Even in the same tumor, the heterogeneity including differences in histology, molecular subtype, pathogenic factors, tumor stage, and degrees of differentiation can also affect the evaluation of the frequency of TP53 mutations [10]. Majority mutations of TP53 cause the loss of function (LOF) of wild-typeTP53 and abrogate their ability to bind on specific DNA motif and perform its tumor-suppressive function. Gain of function (GOF), dominant negative effect on the wild-typeTP53 allele, the loss of heterozygosity of TP53, and interactions with viral proteins also result in the activity loss of wild-typeTP53 [11, 12].

Recent studies using whole-genome and exome sequencing in patients with EC revealed that the most mutated gene is TP53, which means that at least TP53 has a significant influence on EC pathogenicity [13, 14]. In general, TP53 mutations often cause changes on the amino acid sequence of the p53 protein, thus disrupting the function of p53 for tumor inhibition. Under normal conditions, it is difficult to detect the expression of wild-type p53 protein due to its short half-life. Therefore, we usually detect the expression level of p53 protein (mutant-type) using immunohistochemistry (IHC) in clinical diagnosis, but in ESCC, the positive rate of p53 protein expression varies greatly in different reports, ranging from 27 to 75% [15]. At present, the role of p53 as a prognostic marker of ESCC is controversial. To date, the mechanism and correlation between the expression of the p53 protein and mutation of the TP53 gene or prognostic effect in ESCC are limited and contradictory. The clinical value of TP53 mutation and p53 protein expression is worth further exploration.

Herein, we conducted an analysis of TP53 mutation and p53 protein expression in two-large ESCC cohorts, using a series of methods including whole-genome sequencing (WGS), whole exon sequencing (WES), regionally targeted sequencing (TRS) and IHC, to further clarify the association between TP53 mutation status/ the expression of p53 protein and clinicopathologic phenotype and prognosis. This study provided effective and reliable evidence showing TP53 alterations as a biomarker for clinical diagnosis and treatment in ESCC.

2. Materials and Methods

2.1. Patients and Samples

All of the patients and samples were selected from the tissue bank and database of about 500,000 esophageal and gastric cardia carcinomas (1973–2020), established by the State Key Laboratory of Esophageal Cancer Prevention & Treatment and the Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital, Zhengzhou University [16–18]. The database was reviewed to select the present study cohort. All medical records were collected, including detailed clinical, pathological, and survival information. Carcinoma and adjacent noncancerous tissues of 335 patients, which included frozen tissue samples and paraffin tissue samples (for using WGS, WES, and TRS) were collected after surgery. Postoperative tumor paraffin-embedded tissues from 6,252 patients were used for producing tissue microarray (TMA). During the cases of screening, patients were excluded from the database according to the following standard: non-ESCC patients, lacking T, N, M stage, preoperative treatment, incomplete following-up, without tissue for IHC staining in TMA, or failed staining. The detailed flow chart for patient enrollment is shown in Figure 1.

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For the first cohort, a total of 316 surgically resected ESCC tissue specimens (paired primary malignant and adjacent normal) were collected. The tissues were analyzed on a different platform: 316 paired primary malignant and adjacent normal tissues, including 19 cases with frozen tissues that had WGS data available; 92 samples (51 cases of frozen tissue and 41 cases of paraffin-embedded tissues) that had WES data available; and 205 samples (203 cases of frozen tissue and 2 cases of paraffin-embedded tissues) had TRS data available. Among the 316 patients, 276 were used to make TMA. The remaining 40 had no tumor specimen available (owing to a lack of specimen or insufficient tumor cells). The second cohort that contains 6,028 from the 6,252 patients with ESCC TMA was included in the final statistical analysis.

All the patients enrolled for this study were staged using the Union for International Cancer Control (UICC) staging standards, 6th (2002), for esophageal cancer. All patients were followed up after diagnosis until the date of death or December 2019.

2.2. Genomic DNA Extraction

Before DNA extraction, tissue was stained with hematoxylin and eosin (H&E) in order to assess the accurate histopathology for each case. The samples confirmed by a pathologist and in which tumor cells accounted for ≥50%, were chosen for DNA extraction. DNA was isolated from frozen tissue and formalin-fixed, paraffin-embedded (FFPE) tissue. Frozen tissue specimens were collected during surgery, snap-frozen in liquid nitrogen, and stored at −80°C. DNA was extracted from frozen tissue using the phenol-chloroform protocol [19]. For FFPE tissue, DNA was extracted from 8 sections with a 10 μm thickness of each paraffin block using the QIAamp DNA FFPE tissue kit (Qiagen, Hilden, Germany).

2.3. Detection of TP53 Mutation by Next Generation Sequencing

Extracted genomic DNA was examined on agarose gel and by Nanodrop 2000 and Qubit 2.0. Mutations of TP53 were determined using NGS, including WGS, WES, and TRS. DNA library was constructed and captured using TruSeq Nano DNA HT Sample Prep Kit for WGS sequencing, and Agilent SureSelect Human All Exon V6 for WES sequencing, and the SureSelect XT2 Target Enrichment System for the Illumina Multiplexed Sequencing Platform for TRS sequencing following the manufacturer’s recommendations, and then sequenced by an Illumina HiSeqPE150 or Illumina HiSeq 2500 sequencing platform (collaborating with Novogene Bioinformatics Technology Co., Tianjin, China).

2.4. Immunohistochemical Staining for p53 Protein Expression

IHC staining of 4 μm TMA sections was performed by a 2-step protocol using a p53 antibody (1 : 100 dilution) and DAB detection kit (both from Wuhan Servicebio Technology Co., Wuhan, China). In each experiment, both positive and negative controls were included. All images were captured by CaseViewer 2.2 for Windows (3DHISTECH, Budapest, Hungary, Figure 2).

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Figure 2

Immunohistochemical staining scoring criteria for p53 protein in ESCC (A, D, G, J, M, P, S, V, magnification ×80, B, E, H, K, N, Q, T, W, magnification ×200, C, F, I, L, O, R, U, X, magnification ×400). (a–l) Scoring criteria of pattern of immunostaining: (a–c) entirely negative staining; (d–f) scattered; (g–i) focal; (j–l) diffuse. (m–x) Scoring criteria of intensity of immunostaining: (m–o) negative staining; (p–q) weak staining; (s–u) moderate staining; (v–x) strong staining.

2.5. Establishment of Scoring Criterion for p53 Immunohistochemical Staining

The scoring of IHC staining was completed independently by two experienced pathologists. Staining location, intensity, and patterns were reviewed. Tumor cells having a dark brown precipitate in their nuclei were the criterion for a positive reaction. The intensity of staining was grouped into four grades: 0 = entirely negative; 1 = weak; 2 = moderate; 3 = strong. The immunostaining patterns were divided into four terms: 0 = entirely negative; 1 = scattered, meaning only some isolates were positive cells; 2 = focal, where clusters of positive cells were seen in some areas; 3 = diffuse, in which the sheets of positive cells were found throughout most of the areas (Figure 2) [20]. The final results were multiplied by the scores of the immunostaining patterns and staining intensity. Patients were categorized as “high expression (>4)” or “low expression (0∼4)” by the use of IHC scoring criteria.

2.6. Statistical Analyses

Statistical analysis was processed by SPSS for Windows, version 25.0. The T-test and chi-square test or Fisher exact test were used to compare the association of categorical and continuous variables between different groups, respectively. The Kaplan–Meier method analyzed survival tendency and used the log-rank test to compare the survival curves. Cox proportional-hazard models were used for the univariate and multivariate analyses to estimate the hazard ratio of each clinicopathological feature for overall survival (OS). All predictors with value <0.1 in univariate Cox were selected in multivariate Cox analysis. values were 2-tailed and considered statistically significant with less than 0.05.

3. Results

3.1. The Clinicopathological Distributions of ESCC Patients Results

To discover the genetic alteration of TP53 in ESCC, we collected tumor samples to set up two cohorts, including 316 patients for sequencing analysis (276 patients out of 316 patients performed IHC to detect the expression of p53 protein), and 6,028 patients for the expression of p53 protein in TMA, for investigation in this study (Figure 1). Detailed clinicopathological data are listed in Figure 3 and Table 1. The follow-up period of all those patients ranged from 0.08 years to 30.87 years, and the median survival time was 2.98 years.

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3.2. Distribution of TP53 Somatic Mutations and p53 Protein

We evaluated TP53 somatic alterations in the first cohort, and the results showed that 241 of the 316 ESCC patients (76.3%) exhibited a total of 276 TP53 somatic mutations, which included 208 cases having only one mutation and 33 having multiple (two and three) mutations (Tables 2 and 3). TP53 mutations were mainly located in the exon 5–8 (79.4%), meaning that most mutations occurred in the DBD, and very few mutations occurred in the AD1, AD2, and TET domains. Mutations were mostly clustered in exon 5 (23.6%) and exon 8 (23.6%), followed by exon 6 (19.2%) (Figure 4(a)).

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Figure 4

Type of somatic TP53 mutation in ESCC patients. (a) Distribution of TP53 mutations according to the affected exons. (b) Pie chart showing the proportion of the different types of somatic TP53 mutations. (c) Different types of mutations in the spectrum of TP53 mutations. (d) Graphical representation of the site of mutations in the coding sequence of TP53: red lollypop symbols in the lower panel representing missense mutations, blue, yellow, green, pink, and purple lollypop symbols in the upper panel representing others mutations (including nonsense, frameshift del, frameshift ins, silent, mixed type, respectively). Mutations occurring six or more are labeled: TA1, TA2, transactivation domain; DBD, DNA binding domain; TET, tetramerization domain. The figure was generated by the Illustrator for biological sequences version 1.0. (e) Summary of patients’ characteristics and alterations for TP53 and p53 protein in ESCC. Each column represents one patient sample. The phenotypic information in 316 ESCC patients is shown in the upper panel. Alterations for the TP53 gene and p53 protein are shown in the bottom panel.

Next, we investigated the overall pattern of the 276 somatic mutations identified in the TP53, in which 145 were missense (52.5%), 54 were nonsense (19.6%), 33 were splices (12.0%), 24 were frameshift deletion (8.7%), eleven were frame-shit insertion (4.0%), five were silent (1.8%), two were nonframe deletion (0.7%), one was nonframe insertion (0.4%), and one was splice site insertion and deletion (0.4%) (Figure 4(b) and Table 4). For the protein domain distribution of missense mutations, the majority of which occurred in the DBD domain. However, the main type of mutation varied with different exons. In exons 5, 7, and 8, missense mutations accounted for more than 70% (73.8%, 75.0%, and 70.7%, respectively).

We observed that the proportions of the TP53 mutation spectrum were 49/237 (20.7%) for C > A/G > T, 15/237 (6.3%) for C > G/G > C, 117/237 (49.4%) for C > T/G > A, 17/237 (7.2%) for T > A/A > T, 27/237 (11.4%) for T > C/A > G, 12/237 (5.1%) for T > G/A > C. The transition was predominant (144/237, 60.8%), followed by transversion (93/237, 39.2%) (Figure 4(c) and Table 4).

Notably, we found the frequency of nine protein mutations of p53 protein, including four hotspot mutations (p.R175, p.R248, p.R273, and p.R282) and five nonhotspot mutations (p.V173, p.H179, p.R196, p.R213, and p.P278) [21, 22], were more than five, respectively. Hotspot mutations comprised 29.0% (42/145) of all p53 missense mutations (Figure 4(d)).

3.3. Correlation of p53 Protein Expression with TA Class and Align GVGD Classifications

To speculate on the effect of the protein function of the TP53 missense mutation sites, we submitted queries to the IARC TP53 Database (https://p53.iarc.fr) [23–25]. According to TA classification, 145 TP53 missense mutation sites were divided into three categories: functional (4/145, 2.7%), partially functional (11/145, 7.6%), and not-functional (130/145, 89.7%) based on the TA class of the protein function (Table 4). We further observed that four of the 145 sites (in 135 patients) with missense mutations didn’t affect the function of the wild-p53 protein. However, only one of the four sites was a single mutation, and its p53 protein expression was negative. The other three sites were multiple mutations, and their p53 protein expression was positively detected (two were high, and one was low). Among the 130 sites (126 patients) with not-functional (108 patients with p53 protein expression tests), 105 patients were positive p53 protein expression (105/108, 97.2%), including 74 patients with p53 protein high expression (74/108, 68.5%). The p53 protein expression level is consistent with the predicted function based on the mutation sites. In the Align GVGD classification, we did not observe a significant difference in the positive rate of p53 protein in each group.

3.4. Association between TP53 Mutation and Protein Expression with Clinicopathological Parameters and Survival in ESCC

In the first cohort, we did not find any remarkable association between the TP53 mutation and these clinicopathological characteristics in ESCC (Table 2). Although there was no statistically significant difference in survival time when comparing the patients with and without TP53 mutations (Figure 5(a)), surprisingly, the results showed that the patients who had high protein expression of p53 exhibited significantly worse survival than those with low expression when the population narrow down to 276 patients (, Figure 5(b)). The median survival time for 108 patients with high protein expression of p53 and for 168 patients who had low expression was 2.79 ± 0.63 and 5.27 ± 0.52 years, respectively. In addition, no significant difference was observed in the survival time of different mutation types of TP53 (, Figure 5(c)), and no significant difference was observed in survival time between the hotspot and nonhotspot in TP53 missense mutations patients (, Figure 5(d)).

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Figure 5

Kaplan–Meier curves survival analysis of ESCC patients with different TP53 mutations and p53 protein expression. (a) OS of TP53 mutation and wild-type in 316 patients with sequencing data. (b) OS of p53 protein high and low expression in 276 patients who have TP53 mutation and p53 protein expression. (c) OS of different types of mutation in 316 patients with sequencing data. (d) OS of nonhotspot and hotspot mutation in 135 patients who have missense mutation. (e) OS of different groups of TP53 mutation status/p53 protein expression in 276 patients who have TP53 mutation and p53 protein expression. (f) OS of p53 protein high and low expression in 6,028 patients who have p53 expression in TMAs.

To better understand the factors which contribute to the association between TP53 mutations/high p53 expression in patients and survival, we compared the mutation types of p53 patients with ESCC. The high expression of the p53 protein was 66.9% among the 121 patients with TP53 gene missense mutations and only 5.6% in the 36 cases with nonsense mutations. Other types of mutations in the 55 patients (including frame shift, silent, and splice) were 3.6%. Apparently, missense mutation was most likely the cause of p53 protein mutation, leading to the high expression of p53 protein. Other mutation types were possible causes of low expression of the p53 protein. Surprisingly, in the 64 cases of TP53wild-type, high expression of p53 protein accounted for 35.9%, while low expression was 64.1% (41 patients, of which 26 were not expressed at all) (Figure 4(e) and Table 5).

3.5. A New Classification for Evaluating the Association between TP53 Mutation/p53 Protein and Survival

To address the controversial role of TP53 mutation in ESCC, in this study, we established a new classification for evaluating TP53-related survival in ESCC by combining TP53 mutations and p53 protein expression analysis. The results clearly showed that 276 ESCC patients were divided into four groups as follows: TP53 mutation/p53 high expression (85/276, 30.8%), TP53 mutation/p53 low expression (127/276, 46.0%), TP53 wild-type/p53 high expression (23/276, 8.3%), TP53 wild-type/p53 low expression (41/276, 14.9%) (Table 5). Furthermore, the total survival time of the four groups was significantly different (), among which the TP53 mutation/p53 high expression group had the worst survival time, followed by the TP53 wild-type/p53 high expression group (Figure 5(e)).

3.6. Association between p53 Protein Expression and Clinicopathological Changes in Cohort with Large-Scale ESCC Patients

To further validate our discovery of the association between the p53 expression-related prognosis and clinicopathological changes, we assessed additional cohorts to make an analysis (Table 6). The positive expression of p53 protein was observed in 3,562 (59.1%) ESCC patients, of which 1,819 patients (51.1%) showed high expression of p53 protein. Moreover, the high expression of p53 protein in high-incidence areas was more common than in low-incidence areas with a rate and value of 31.5% vs. 27.7%, . High expression of p53 protein was closely correlated with poor tumor differentiation (). The frequency of high expression of p53 protein in the early ESCC (0 + I stage) was 1.27-fold higher than that in the advanced ESCC (38.2% vs. 30.0%, ). Furthermore, the rate of positive cancer embolus in high-expression groups was 1.31-fold higher than in low-expression groups (5.1% vs. 3.9%, ).

3.7. Independent Prognosis Marker Role of p53 Protein on Survival Analysis in 6,028 ESCC Patients

To determine whether a high expression level of the p53 protein could be used as a prognosis marker in patients with ESCC, we conducted the analysis using the p53 expression in 6,028 ESCC patients. In this cohort, the analysis showed median OS time in patients with p53 high expression and with low was 2.71 ± 0.09 and 3.08 ± 0.06 years, respectively. The presence of high expression of p53 protein was significantly associated with decreased OS (, Figure 5(f)).

3.8. Cox Univariate and Multivariate Regression Analyses

Cox proportional-hazard models were chosen for the univariate and multivariate analyses. The univariate Cox regression analysis demonstrated that the sex, age, high/low incidence area, cigarette smoking, alcohol consumption, location, differentiation, T stage, N stage, M stage, UICC stage, cancer embolus, and p53 protein high/low expression were dependent prognostic factors (, Figures 6(a), 6(b), and 7). In multivariate Cox regression analysis, as compared with the low expression of p53 protein, the patients with high expression (HR = 1.134, 95% CI 1.065 to 1.207) were associated with decreased survival after adjustment for the age, high/low incidence area, cigarette smoking, differentiation, and T and N stages as independent prognostic factors (, Figure 6(c)).

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3.9. Stratified Survival Analysis by within Independent Prognostic Factors

We performed a stratified analysis to eliminate the influence of the independent prognostic factors on evaluating the expression of the p53 protein on prognosis. When patients were stratified according to high/low incidence area, cigarette smoking, and N stages, the consistent trend of OS was observed in different stratification (Figures 8(a)–8(f)). When stratified according to patients’ age at diagnosis, degree of differentiation, and T stage, we only found that patients the high expression of p53 protein in >60 (, Figure 8(g)), medium and low differentiation (, Figures 8(h) and 8(i)), and T3 (, Figure 8(j)) had shorter OS than those with low expression.

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Figure 8

Kaplan–Meier curves survival analysis of ESCC patients with high/low p53 protein expression after stratification based on independent prognostic factors. OS of patients with p53 protein high vs. low expression in patients with high-incidence area (a) and low-incidence area (b). OS of patients with p53 protein high vs. low expression in patients with (c) and without cigarette smoking (d). OS of patients with p53 protein high vs. low expression in patients with N0 (e) and N1 stage (f). OS of patients with p53 protein high vs. low expression in patients >60 years (g). OS of patients with p53 protein high vs. low expression in patients with moderate (h) and poor differentiation (i). OS of patients with p53 protein high vs. low expression in patients with T3 stage (j).

4. Discussion

We have conducted a two-large ESCC cohort analysis to evaluate the association between TP53 mutation statuses/the expression of the p53 protein and clinicopathological features and prognosis in ESCC patients. Most importantly, our results further confirmed that the expression of the p53 protein can reflect the prognosis of ESCC patients by using a large number of tissue samples, and high p53 expression indicates a poor prognosis. However, the mutation of the TP53 gene has no obvious correlation with prognosis, which is controversial in previous reports.

With the rapid development of sequencing technology, the prognostic value of TP53 has been confirmed in a variety of tumors, and the mutation of TP53 indicates a poor prognosis across different types of human cancers. In this study, despite the high frequency of TP53 mutations, there is no obvious evidence showing its association with clinicopathological features and prognosis in patients with ESCC. Previously, other colleagues showed that TP53 status was correlated with tumor invasion depth, TNM stage, lymph node metastasis, distant metastasis, and differentiation degree [15]. Meanwhile, the survival time of TP53 gene mutation and p53 protein overexpression was shorter than the control group in ESCC [15]. However, Zhao et al. showed that the high expression of p53 protein was an independent prognostic factor for survival, while the mutation of the TP53 gene was unrelated to prognosis [26]. The conflicting results might be caused by the following reasons: of the limited number of patients, insufficient clinical follow-up, different experimental techniques, mutation sites on different target exons, and variable factors.

We further found that the frequency of the TP53 mutation was 76.3% in ESCC, which was between 66.7% and 82.7% of the frequency of the TP53 mutation in EC by NGS, as previously reported [27–30]. Cui et al. published WGS results for 508 cases of ESCC, which also showed a similar TP53 mutation frequency of 74.8% [30]. The cause might be that the study population and histopathological type were different. Our study confirmed that the main mutation type of TP53 in ESCC patients is a missense mutation, which may play a pivotal role in tumorigenesis because the p53 protein usually has positive expression, or even higher expression, owing to the accumulation of a nonfunctional protein that loss activity as a tumor suppressor, and some of which exert trans-dominant repression over the wild-type counterpart [31, 32]. C > T/G > A Transition is the predominant mutation of TP53 gene mutations in our study, and it is a typical marker associated with betel quid chewing, tobacco use, and alcohol drinking in oral squamous cell carcinoma in Taiwanese [33]. C > A/G > T transversion is a typical feature of carcinogen exposure associated with cigarette smoking, which is the most risk factor for ESCC and lung cancer. This transversion occurred at the sites of adduct formation for the metabolites of benzo (a) pyrene, a major tobacco carcinogen (codons 157, 248, and 273) [34, 35].

To our knowledge, this is the largest sample-scale study that has examined p53 protein expression by IHC. Our study herein showed that the expression of p53 protein was associated with high/low incidence area, degree of differentiation, and cancer embolus, and revealed that high expression of p53 protein is an independent prognostic factor rather than TP53 gene mutation, with 1.134-fold mortality risk. In previous studies, the correlation between p53 protein expression, clinicopathological features, and prognosis was significantly different [36–40]. To date, only 13 of the 30 articles have suggested that p53 protein was an adverse factor for prognosis, and the rest of the reports suggested that p53 expression had no effect on prognosis [39]. Wang et al. revealed that a more advanced TNM stage, positive lymph node metastasis, and distant metastasis were associated with p53 protein expression [41]. Although there were no significant differences between p53 expression and T stage, N stage, and M stage in our finding, our further validation of 6,028 patients demonstrated a significantly strong correlation between the expression of p53 protein and clinical phenotype and prognosis. The judgment for the p53 protein expression is different because of the variety of using antibodies and experimental methods used in IHC. In addition to the source of the tumors and the existence of tumor heterogeneity, the difference in evaluation criteria is the most critical factors leading to inconsistent conclusions in previous studies [36, 37, 40].

Our study showed that the expression of p53 protein in the high-incidence areas of EC was significantly higher than in low. The existence of high/low incidence areas of EC is one of the prominent epidemiological features of ESCC, suggesting that environmental factors play an important role in the pathogenesis of ESCC. In high-incidence areas of EC, exposure to environmental carcinogens (such as nitrite and mold-contaminated foods) may lead to carcinogenesis of the esophageal squamous epithelium. TP53 is one of the most susceptible genes to environmental carcinogens in the process of tumor induction. Aflatoxin B1 (AFB1) are potent carcinogens, which induces an arginine to serine at a single base substitution at the third base of codon 249 in TP53 (G > T transversion) [42, 43]. N-nitroso compounds (NOC) are an alkylating agent and cause guanine alkylation to generate O⁶-alkylguanine, which results in C > T/G > A transition during DNA replication when paired with thymine [44–46]. The DNA repair protein O⁶-alkylguanine-DNA alkyltransferase (AGT) specifically repairs O⁶-alkylguanine adducts in DNA. However, AGT mutations occurred more frequently in patients in high-incidence areas of esophageal cancer than in the normal population [47].

In ESCC, it is not feasible to determine the presence of TP53 gene mutations through the loss of p53 protein expression. When the TP53 gene is mutated, it will cause different changes in proteins. Nonsense, frameshift, silent, and splice mutations usually cause protein truncation and make the synthesis of normal p53 protein interfere in order to cause LOF, and p53 protein is not expressed either [21]. The situation became complicated when a missense mutation occurred because the p53 protein can be caused by various missense mutations. Some will still have the function of wild-type p53 protein, and some will only retain part of the function and acquire gain GOF, which is associated with malignancy, invasion, and metastasis [48]. Due to the broad existence of LOF and GOF of the TP53 mutation, false-positive or false-negative levels of p53 protein will be detected by using IHC in clinical applications, which might not be a reliable method for evaluating the function of the TP53 mutation [49]. Our observation confirmed that immunohistochemical detection of p53 protein expression could more effectively reflect the relationship with prognosis and clinicopathologic features than the mutation status of the TP53 gene in ESCC, which was further supported by studies with a larger sample.

The limitation of this study is the representativeness and accuracy of the application of TMA-based IHC showing the expression of p53 protein with extreme heterogeneity. To conclude, laboratory techniques need to be used to detect p53 protein in TMAs, and the criterion of IHC evaluation must be constant. Limited tissue sampling cannot effectively reflect the real status of the whole section because of tumor heterogeneity. Our research simultaneously detected p53 protein in a part of the original pathological sections made TMAs to reduce potential limits, and used the same scoring criteria for judgment. The results are no different from those in TMAs.

In conclusion, TP53 is the most mutated gene in ESCC, showing great potential to be a diagnostic biomarker in treating cancer. However, the role of TP53 mutation and expression of p53 in ESCC still remains unclear. Our study, involving two large-scale cohorts, was the first to conduct genomic profiling and expression of the p53 protein, while demonstrating the association with clinical phenotypes and prognosis. Our findings show that the expression of p53 protein is more effective in predicting clinicopathological features and prognosis, is a valid biomarker of an unfavorable prognosis, and will contribute to clinical diagnosis, prognosis prediction, and targeted therapy.

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Ethical Approval

The study protocol was approved by the Research Ethics Committee of The First Affiliated Hospital of Zhengzhou University.

All patients provided written informed consent to participate.

Conflicts of Interest

The authors declare that they have no conflicts of interest for this article.

Authors’ Contributions

Yan Jin conceptualized and designed the study, performed data analysis, and wrote the draft and revised the manuscript. Lidong Wang conceptualized and designed the study and revised the manuscript. Xueke Zhao, Xin Song, Ran Wang, Zongmin Fan, Panpan Wang, and Miaomiao Yang performed data collection, interpretation, follow-up, and performed the experiment. Fuyou Zhou and Qide Bao performed data collection, interpretation, and follow-up. All authors approved the final manuscript.

Acknowledgments

The authors thank Dr. Liang Wang (Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, USA.) and Fujun Qin (Molecular Pathology Centre, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China) for helping them publish the manuscript in English and thoughtful comments for this manuscript. We thank Dr. Xuemin Li (Department of Pathology, Cixian Hospital, Hebei province, China) and Jilin Li (Department of Pathology, Linzhou Esophageal Cancer Hospital, Henan province, China) for their assistance in pathologic diagnosis and IHC scoring. This work was supported by the National Key R&D Program “Precision Medicine” of China (2016YFC0901403), the National Natural Science Foundation of China (Grant No. 81872032, U1804262), the Major Science and Technology Projects of Henan Province (161100311300), and the Doctoral Team Foundation of the First Affiliated Hospital of Zhengzhou University (2016-BSTDJJ-03).

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Copyright © 2023 Yan Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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