# Immunological Role ofTP53 Somatic Mutation Classification in Human Cancers

Authors: Jianfei Fang; Ying Yang; Lina Xie; Wenjuan Yin
Journal: Journal of Oncology
 (2023)

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## Abstract

Background. TP53 is a very common tumor suppressor gene and has implicated in various cancers. A systematic immunological analysis of TP53 somatic mutation classification in multiple cancers is still lacking. Methods. To assess the immunological value of TP53 somatic mutation classification in various cancers, we integrated a series of bioinformatics methods to analyze the role of TP53 gene across the public databases, such as UCSC Xena, Cancer Cell Line Encyclopedia (CCLE), Ensembl, and Genotype−Tissue Expression (GTEx). Results. The results revealed that the TP53 expression level had significant difference in tumor tissues and normal tissues, and it had a high expression level in most malignant tumors. Moreover, the missense mutation is the most common type of TP53 mutation in most cancers. In addition, the Cox proportional hazards model analysis and Kaplan−Meier (KM) survival analysis demonstrated that the TP53 expression is a high-risk factor in brain lower-grade glioma (LGG), prostate adenocarcinoma (PRAD), and uterine carcinosarcoma (UCS), which is opposite in uterine corpus endometrial carcinoma (UCEC). Besides, compared to the TP53 nontruncating mutation classification samples, we found that TP53 truncating mutation samples had lower TP53 expression levels in certain types of cancer. Notably, TP53 was associated with the mismatch repair (MMR) gene in some cancers which contained truncating or nontruncating mutation. Based on the classification of truncating or nontruncating mutation, we also discovered that TP53 expression was positively or negatively correlated with the immune score, stromal score, and the levels of immune cell infiltration in different cancers. Conclusions. Our research reveals an overarching landscape of immunological value on TP53 status in various malignant tumors. According to our results, we demonstrate that TP53 also plays an immunological role in various cancers.


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## Body

## 1. Introduction

Cancer is a malignant disease with a high mortality rate. Up till now, there is no effective treatment to absolutely cure cancer patients which most likely predicts poor quality of life [1]. However, immunotherapy has been reported to achieve great improvement and regarded as one of the major breakthroughs in cancer treatments [2–4]. It is feasible to seek out potential immunotherapy biomarker through the utility of databases which contained sequencing data.TP53 is a well-studied tumor suppressor gene and is always the hotspot of tumor research. Nowadays, studies on TP53 function have concentrated on the correlation between variation pattern of TP53 and prognosis [5–7]. TP53 has a prominent role in preventing tumor development and maintaining genomic stability (GS) [8, 9]. The cancer-associated function of the p53 protein depends on its five function domains, including transactivation domain, proline rich domain, DNA-binding domain, oligomerization domain, and carboxy-terminal regulatory domain [10, 11]. In various cancers, TP53 was explicitly linked to cancer development and prognosis [12–14], but these conclusions were still controversial [15]. The most widely accepted concept is that the nontruncated mutation commonly induces high expression of TP53 [13]. Previous studies have demonstrated that TP53 mutations are associated with their expression, cancer prognosis [13], and immune − related research [16]. Integrated studies combined with these features with classification of TP53 somatic mutation are few.Our work makes it possible to explore the association betweenTP53 mutation classification and immunological function by multiple databases, such as UCSC Xena, CCLE, Genotype – Tissue Expression (GTEx), and Ensembl. This study may provides new perspective on the relationship between gene variation classification and microenvironment for researchers. We present the following article in accordance with the MDAR reporting checklist.


## 2. Methods

### 2.1.TP53 Gene Expression Data

The expression data across 33 types of cancers which contains 11057 samples were downloaded from UCSC Xena. The expression data across 31 health tissues which contains 7858 samples and 28 tumor cell lines were downloaded from GTEx and CCLE. Expression data were extracted using in-house Perl scripts and further analysis using R software (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria).


### 2.2.TP53 Variation and Classification Criteria

In this part, the varscan2 variation files withTP53 gene form UCSC Xena (accessed 18 February 2022) were used for analyzing base alterations. According to the mutation classification method in previous studies [13, 15], we distinguished between truncating (including nonsense mutation, frameshift mutation, and splice site mutation) and nontruncating (including missense mutation, in-frame deletion mutation, and in-frame insertion mutation) TP53 mutation.


### 2.3. Correlation betweenTP53 and Prognosis

We obtained relevant clinical data from the UCSC Xena. Overall survival (OS) data of patients were used to estimate the prognosis status of differentTP53 classification. The patients were divided into two sections by the mean value of TP53 expression as a cutoff value [17]. The KM method and log-rank test were used for survival analysis. Moreover, Cox proportional hazards model analysis was performed to analyze the hazard ratio among different types of cancer.


### 2.4. Correlation betweenTP53 and Mismatch Repair Genes

We calculated the Pearson correlation coefficient to estimate the relationship betweenTP53 and mismatch repair (MMR) genes, including MLH1, MSH2, MSH6, and PMS2. P<0.05 was considered for the statistical significance.


### 2.5. Correlation betweenTP53 and Immunity

We used the ESTIMATE algorithm to infer the degree of tumor infiltration [18, 19]. Moreover, the CIBERSORT method was further used for exploring the correlation between TP53 and immunity [20]. The annotation file with Ensembl gene converted to gene symbol was downloaded by Ensembl database.


### 2.6. Correlation betweenTP53 and Biological Function

It is convenient to investigate the function ofTP53 in various cancers using GSEA package. All analyses were completed by R software (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria).


### 2.7. Statistical Analysis

The gene expression values were converted to fragments per kilobase per million for subsequent analysis.P<0.05 was considered for the statistical significance. All statistical analysis was processed by the R software (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria).


## 2.1.TP53 Gene Expression Data

The expression data across 33 types of cancers which contains 11057 samples were downloaded from UCSC Xena. The expression data across 31 health tissues which contains 7858 samples and 28 tumor cell lines were downloaded from GTEx and CCLE. Expression data were extracted using in-house Perl scripts and further analysis using R software (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria).


## 2.2.TP53 Variation and Classification Criteria

In this part, the varscan2 variation files withTP53 gene form UCSC Xena (accessed 18 February 2022) were used for analyzing base alterations. According to the mutation classification method in previous studies [13, 15], we distinguished between truncating (including nonsense mutation, frameshift mutation, and splice site mutation) and nontruncating (including missense mutation, in-frame deletion mutation, and in-frame insertion mutation) TP53 mutation.


## 2.3. Correlation betweenTP53 and Prognosis

We obtained relevant clinical data from the UCSC Xena. Overall survival (OS) data of patients were used to estimate the prognosis status of differentTP53 classification. The patients were divided into two sections by the mean value of TP53 expression as a cutoff value [17]. The KM method and log-rank test were used for survival analysis. Moreover, Cox proportional hazards model analysis was performed to analyze the hazard ratio among different types of cancer.


## 2.4. Correlation betweenTP53 and Mismatch Repair Genes

We calculated the Pearson correlation coefficient to estimate the relationship betweenTP53 and mismatch repair (MMR) genes, including MLH1, MSH2, MSH6, and PMS2. P<0.05 was considered for the statistical significance.


## 2.5. Correlation betweenTP53 and Immunity

We used the ESTIMATE algorithm to infer the degree of tumor infiltration [18, 19]. Moreover, the CIBERSORT method was further used for exploring the correlation between TP53 and immunity [20]. The annotation file with Ensembl gene converted to gene symbol was downloaded by Ensembl database.


## 2.6. Correlation betweenTP53 and Biological Function

It is convenient to investigate the function ofTP53 in various cancers using GSEA package. All analyses were completed by R software (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria).


## 2.7. Statistical Analysis

The gene expression values were converted to fragments per kilobase per million for subsequent analysis.P<0.05 was considered for the statistical significance. All statistical analysis was processed by the R software (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria).


## 3. Results

### 3.1. The Landscapes ofTP53 Expression across Multiple Datasets

In various normal samples, we found significant difference ofTP53 expression, as shown in Figure 1(a) (P<2.2e−16). The skin tissue expresses TP53 on the highest level and the brain tissue expressed the lowest. We further extracted TP53 expression values from cancer cell lines using CCLE datasets, and the consequence was presented in Figure 1(b), displaying the analogous difference in cell lines (P=1.9e−07).Figure 1
The expression levels ofTP53 in GTEx datasets and CCLE datasets. (a) TP53 expression in normal tissues using GTEx datasets. (b) TP53 expression in tumor cell lines using CCLE datasets.
(a)(b)In addition, we discovered the similar significance ofTP53 expression in 33 cancer types (P<2.2e−16). As shown in Figure 2(a), pheochromocytoma and paraganglioma (PCPG) and kidney chromophobe (KICH) are on the lowest level while UCEC is on the highest level.Figure 2
Expression level ofTP53 in UCSC Xena datasets. (a) TP53 expression in tumor samples. (b) TP53 expression in tumor-normal samples. ∗indicates P<0.05, ∗∗indicates p<0.01, ∗∗∗indicates P<0.001.
(a)(b)To further investigate the difference among tumor-normal samples, we analyzed the UCSC Xena data. In view of the fact that some types of cancers included only a few normal samples (for instance, less than 10 or no normal samples datasets), 16 types of cancer were retained for further analysis (Figure2(b)). Compared with normal samples, we observed higher expression of TP53 in most cancer types, for instance, lung squamous cell carcinoma (LUSC, P=0.0001894), rectum adenocarcinoma (READ, P=0.007547), stomach adenocarcinoma (STAD, P=1.229e−06), colon adenocarcinoma (COAD, P=4.4e−09), kidney renal papillary cell carcinoma (KIRP, P=3.848e−12), uterine corpus endometrial carcinoma (UCEC, P=1.655e−08), prostate adenocarcinoma (PRAD, P=0.0003865), bladder urothelial carcinoma (BLCA, P=0.0321), lung adenocarcinoma (LUAD, P=3.794e−09), liver hepatocellular carcinoma (LIHC, P=5.799e−06), kidney renal clear cell carcinoma (KIRC, P<2.2e−16), thyroid cancer (THCA, P=4.995e−12), and esophageal cancer (ESCA, P=0.01165). In contrast, TP53 expression was downregulated in kidney chromophobe (KICH, P=1.145e−07). Moreover, there were no significant difference in TP53 expression levels in breast invasive cancer (BRCA, P=0.2613) and head and neck squamous cell carcinoma (HNSC, P=0.9583).


### 3.2.TP53 Mutation in Pan-Cancer Cohorts

After eliminating those cancers with the small number samples (for instance, cancer types with less than thirty samples were excluded), 19 cancer types were screened for depicting theTP53 mutation types in pan-cancer, and the landscape was exhibited in Figure 3. Consistent with the previous studies [13, 21], we found that missense mutations accounted for the most of TP53 variants in 19 types of cancer. Next, nonsense mutation ranked second.Figure 3
Landscape of somatic mutations ofTP53 gene in pan-cancer using the UCSC Xena cohort.


### 3.3. Prognostic Impact ofTP53 across TP53-Mut Cancers

In Figure4, forest plots showed that TP53 was high-risk gene in LGG (hazard ratio = 1.614), PRAD (hazard ratio = 11.97), and UCS (hazard ratio = 2.748), while it was a low-risk gene in UCEC (hazard ratio = 0.5717). Furthermore, we carried out prognostic impact of TP53 expression using mean value as criteria and revealed the significant difference between high and low expression groups among LGG (P=0.0084), PRAD (P=0.0035), UCS (P=0.0098), and UCEC (P=0.0068).Figure 4
Association betweenTP53 expression and overall survival time across TP53-mut cancer types. ∗indicates P<0.05, ∗∗indicates P<0.01, ∗∗∗indicates P<0.001.


### 3.4.TP53 Mutation Classification and Survival

We distinguishedTP53 mutations into truncating and nontruncating classes to observe their effects on TP53 expression. In line with the earlier conclusion, the results (see Figure 5) showed that TP53 expression levels were upregulated in truncating mutation relative to nontruncating patients [15].Figure 5
Association betweenTP53 mutation classification and overall survival time in TP53-mut cancers. ∗indicates P<0.05, ∗∗indicates P<0.01, ∗∗∗indicates P<0.001.Furthermore, we performed survival analysis for the sake of the evaluation ofTP53 mutation’s prognostic value. Unfortunately, no significance difference was detected between TP53 mutation types and overall survival time in any type of cancer (Figure 5). However, KM (Kaplan−Meier) results indicated a clear trend that individuals with truncating mutation had longer survival time in specific cancers, such as READ, BLCA, and PAAD.


### 3.5. Correlation betweenTP53 and Mismatch Repair Genes

Mismatch Repair (MMR) is a typical DNA repair mechanism [22]. Ectopic expression of MMR genes might induce the high frequency of somatic mutations [23, 24]. In truncating TP53-mut cancer types (see Figure 6(a)), we examined that MMR genes had significant positive correlation with HNSC (MLH1 correlation coefficient/P-value = 0.27/0.00096; MSH6 correlation coefficient/P-value = 0.17/0.04), LIHC (MSH2 correlation coefficient/P-value = 0.47/0.003; PMS2 correlation coefficient/p-value = 0.33/0.04; MSH6 correlation coefficient/P-value = 0.41/0.0104), and LGG (MLH1 correlation coefficient/P-value = 0.28/0.047; MSH2 correlation coefficient/P-value = 0.31/0.02; MSH6 correlation coefficient/P-value = 0.35/0.012). In contrast, negative correlation with TP53 expression was discovered in four cancers, including COAD (MLH1 correlation coefficient/P-value = −0.42/0.0009), UCEC (MLH1 correlation coefficient/P-value = −0.47/0.0005), SKCM (PMS2 correlation coefficient/P-value = −0.5/0.0097), and STAD (PMS2 correlation coefficient/P-value = −0.25/0.04).Figure 6
Association betweenTP53 expression and MMR genes in 19 TP53-mut cancers. (a) The relationship between TP53 expression and MMR genes in truncating TP53-mut cancers. (b) The relationship between TP53 expression and MMR genes in nontruncating TP53-mut cancers. For each cell, the top left triangle represents the P value and the bottom right triangle represents the correlation coefficient. ∗indicates P<0.05, ∗∗indicates P<0.01, ∗∗∗indicates P<0.001.
(a)(b)In nontruncatingTP53-mut cancer types (Figure 6(b)), we also explored the positive correlation across multiple cancers. Interestingly, compared with different TP53 nontruncating classification, there was opposite correlations in UCEC (MSH6 correlation coefficient/P-value = 0.24/0.005), SKCM (MSH6 correlation coefficient/P-value = 0.37/0.03), and STAD (MSH2 correlation coefficient/P-value = 0.43/3.267e − 05; PMS2 correlation coefficient/P-value = 0.32/0.003; MSH6 correlation coefficient/P-value = 0.38/0.0003).


### 3.6. Correlations betweenTP53 and Immunity

Tumor microenvironment (TME) played an important role during neoplasm occurrence and progression [16, 25, 26]. In this study, we calculate the stromal score, immune score, and estimate score in 19 TP53-mut cancers using the ESTIMATE method. As shown in Table 1, in TP53 truncating mutation cancers, such as BRCA, HNSC, LIHC, LUAD, LUSC, SKCM, PAAD, SARC, and STAD, TP53 was significantly positively correlated with the immune score, as well as stromal score and estimate score, while it was negatively correlated with UCS. Conversely, in nontruncating mutation cancers (for instance, BRCA, GBM, OV, and PRAD), TP53 was significantly negatively associated with the estimated TME scores, while LIHC and PAAD had the opposite consequences.Table 1
Correlation betweenTP53 expression and tumor microenvironment in 19 TP53-mut cancers.
Cancer typesTP53 classificationCor/P-valueStromal scoreImmune scoreEstimate scoreBRCATruncating0.32/0.0003∗∗∗0.3/0.0006∗∗∗0.35/5.607e − 05∗∗∗Nontruncating−0.4/7.994e − 09∗∗∗0.07/0.34−0.2/0.02∗BLCATruncating0.11/0.370.13/0.280.13/0.28Nontruncating−0.05/0.56−0.2/0.07−0.1/0.2COADTruncating0.09/0.510.16/0.220.13/0.31Nontruncating−0.1/0.22−0.12/0.14−0.1/0.16UCECTruncating0.13/0.350.03/0.850.08/0.6Nontruncating−0.1/0.21−0.16/0.053−0.15/0.07ESCATruncating0.08/0.58−0.08/0.590.0009/0.995Nontruncating−0.01/0.92−0.21/0.06−0.11/0.31GBMTruncating0.07/0.840.16/0.640.13/0.71Nontruncating−0.3/0.07−0.32/0.053−0.33/0.04∗HNSCTruncating0.3/0.0002∗∗∗0.3/0.0002∗∗∗0.32/7.295e − 05∗∗∗Nontruncating0.1/0.180.04/0.560.08/0.29LIHCTruncating0.45/0.004∗∗0.42/0.007∗∗0.47/0.002∗∗Nontruncating0.15/0.210.24/0.046∗0.22/0.07LGGTruncating0.14/0.32−0.14/0.32−0.01/0.94Nontruncating−0.09/0.220.03/0.72−0.02/0.81LUADTruncating0.27/0.009∗∗0.3/0.005∗∗0.31/0.003∗∗Nontruncating−0.03/0.72−0.09/0.3−0.07/0.43LUSCTruncating0.28/0.002∗∗0.25/0.004∗∗0.28/0.0014∗∗Nontruncating−0.05/0.4−0.07/0.29−0.07/0.31SKCMTruncating0.7/6.962e − 05∗∗∗0.8/1.285e − 06∗∗∗0.81/5.301e − 07∗∗∗Nontruncating−0.09/0.62−0.15/0.39−0.13/0.45OVTruncating0.15/0.16−0.006/0.950.08/0.47Nontruncating−0.17/0.044∗−0.13/0.11−0.16/0.054PAADTruncating0.32/0.090.47/0.0105∗0.43/0.02∗Nontruncating0.23/0.110.4/0.004∗∗0.34/0.02∗PRADTruncating−0.08/0.750.08/0.76−0.005/0.98Nontruncating−0.36/0.04∗−0.24/0.18−0.32/0.07READTruncating0.36/0.060.26/0.180.34/0.08Nontruncating−0.08/0.530.05/0.68−0.02/0.87SARCTruncating0.52/0.002∗∗0.24/0.170.4/0.02∗Nontruncating0.08/0.560.008/0.960.04/0.8STADTruncating0.46/0.0001∗∗∗0.27/0.03∗0.4/0.0008∗∗∗Nontruncating−0.01/0.92−0.06/0.61−0.04/0.74UCSTruncating−0.61/0.04∗−0.13/0.7−0.43/0.17Nontruncating−0.07/0.70.2/0.240.09/0.62∗indicates P<0.05; ∗∗indicates P<0.01; ∗∗∗indicates P<0.001.Subsequently, we investigated the immune cell infiltration levels amongTP53-mut caners. The results implied the diverse significant correlation between levels of immune cell infiltration and TP53 expression among TP53 truncating or nontruncating mutant cancers. Significant correlation is screened and presented in Table 2.Table 2
Significant correlation betweenTP53 expression and immune cell infiltration in 19 TP53-mut cancers.
Cell typesTP53 classification in 19 TP53-mut cancers (cor/P-value)TruncatingNon-truncatingNaive B cellsUCS: 0.65/0.02∗BLCA: 0.22/0.02∗LGG: −0.23/0.002∗∗LUAD: 0.18/0.03∗Memory B cellsPAAD: 0.38/0.045∗NAPlasma cellsNALGG: −0.17/0.02∗CD8 T cellsUCEC: 0.41/0.003∗∗BRCA: 0.22/0.002∗∗SKCM: 0.45/0.02∗LIHC: 0.25/0.04∗LUAD: 0.18/0.04∗LUSC: 0.16/0.012∗Naive CD4 T cellsHNSC: −0.2/0.04∗LGG: −0.18/0.02∗OV: 0.37/0.0002∗∗∗READ: −0.3/0.02∗Resting CD4 memory T cellsUCEC: −0.37/0.007∗∗BRCA: −0.2/0.007∗∗PAAD: −0.4/0.009∗∗PRAD: −0.4/0.04∗Activated CD4 memory T cellsNABRCA: 0.3/1.872e − 05∗∗∗GBM: −0.4/0.02∗Follicular T helper cellsUCEC: 0.34/0.014∗BRCA: 0.3/1.876e − 05∗∗∗OV: −0.24/0.02∗LGG: 0.16/0.03∗STAD: 0.3/0.013∗Regulatory T cellsLUSC: 0.22/0.011∗LUSC: 0.14/0.03∗SKCM: 0.44/0.03∗Gamma delta T cellsOV: 0.36/0.0003∗∗∗GBM: −0.33/0.04∗LUAD: −0.22/0.009∗∗PAAD: 0.65/3.765e − 07∗∗∗PRAD: 0.4/0.02∗Resting NK cellsBLCA: 0.31/0.013∗BRCA: −0.2/0.02∗SKCM: −0.4/0.04∗READ: −0.3/0.03∗OV: 0.34/0.0007∗∗∗SARC: 0.36/0.011∗Activated NK cellsNACOAD: 0.22/0.008∗∗UCEC: −0.18/0.03∗GBM: 0.49/0.002∗∗LGG: 0.23/0.002∗∗PRAD: 0.46/0.007∗∗M0 macrophagesESCA: 0.4/0.004∗∗UCEC: 0.21/0.014∗LUSC: 0.22/0.02∗OV: 0.25/0.002∗∗OV: 0.24/0.02∗M1 macrophagesNABRCA: 0.14/0.045∗BLCA: −0.2/0.047∗PAAD: 0.3/0.03∗PRAD: −0.43/0.013∗M2 macrophagesBRCA: −0.25/0.005∗∗BRCA: −0.4/8.285e − 09∗∗∗LGG: −0.31/0.03∗HNSC: 0.21/0.006∗∗UCS: −0.6/0.046∗LUAD: −0.2/0.04∗Resting dendritic cellsSKCM: 0.39/0.047∗NAActivated dendritic cellsBRCA: −0.23/0.008∗∗BRCA: 0.15/0.04∗OV: −0.2/0.03∗HNSC: −0.2/0.014∗PRAD: 0.6/0.008∗∗Resting mast cellsCOAD: 0.4/0.002∗∗BRCA: −0.4/3.896e − 08∗∗∗LIHC: −0.25/0.04∗SARC: −0.35/0.014∗Activated mast cellsHNSC: −0.2/0.004∗∗BRCA: 0.21/0.003∗∗LIHC: 0.37/0.02∗HNSC: −0.3/0.0001∗∗∗LUAD: −0.2/0.03∗LUSC: −0.1/0.047∗UCS: 0.44/0.008∗∗EosinophilsNABRCA: 0.14/0.04∗COAD: −0.2/0.04∗LGG: 0.18/0.02∗SKCM: −0.5/0.004∗∗NeutrophilsHNSC: −0.2/0.02∗COAD: −0.22/0.006∗∗STAD: −0.26/0.03∗HNSC: −0.2/0.008∗∗LUAD: −0.2/0.012∗LUSC: −0.22/0.0006∗∗∗MonocytesNACOAD: −0.2/0.04∗GBM: −0.36/0.03∗OV: −0.25/0.002∗∗STAD: −0.27/0.011∗∗indicates P<0.05; ∗∗indicates P<0.01; ∗∗∗indicates P<0.001. NA indicates no statistical correlation.


### 3.7. Correlation betweenTP53 and Biological Function

We carried out a thorough inspection of Gene Set Enrichment Analysis (GSEA) to investigate the relationship betweenTP53 and biological function inTP53-mut tumor tissues, and the results are shown in Supplementary Figures (Available here).InTP53 truncating vs nontruncating mutation cancers, the KEGG data indicated that TP53 positively regulated RIG-I-like receptor signal pathway [27] and cytosolic DNA-sensing pathway [28, 29] in BLCA and OV. In contrast, TP53 was predicted to be a negative regulator of the T cell receptor signaling pathway [30], cytosolic DNA-sensing pathway and RIG-I-like receptor signal pathway in GBM, and/or LUSC (Figure 7). In GBM and UCS, the GO data showed that TP53 expression was negatively correlated with adaptive immune response, immune response − regulating signal pathway and had a positive regulation with immune response. In READ, TP53 expression exhibited the opposite effect (see Figure 8).Figure 7
KEGG analysis ofTP53 in BLCA, OV, GBM, and LUSC. Peaks on the upward curve indicate positive regulation and peaks on the download curve indicate negative regulation.Figure 8
GO analysis ofTP53 in READ, GBM, and UCS. Peaks on the upward curve indicate positive regulation and peaks on the download curve indicate negative regulation.


## 3.1. The Landscapes ofTP53 Expression across Multiple Datasets

In various normal samples, we found significant difference ofTP53 expression, as shown in Figure 1(a) (P<2.2e−16). The skin tissue expresses TP53 on the highest level and the brain tissue expressed the lowest. We further extracted TP53 expression values from cancer cell lines using CCLE datasets, and the consequence was presented in Figure 1(b), displaying the analogous difference in cell lines (P=1.9e−07).Figure 1
The expression levels ofTP53 in GTEx datasets and CCLE datasets. (a) TP53 expression in normal tissues using GTEx datasets. (b) TP53 expression in tumor cell lines using CCLE datasets.
(a)(b)In addition, we discovered the similar significance ofTP53 expression in 33 cancer types (P<2.2e−16). As shown in Figure 2(a), pheochromocytoma and paraganglioma (PCPG) and kidney chromophobe (KICH) are on the lowest level while UCEC is on the highest level.Figure 2
Expression level ofTP53 in UCSC Xena datasets. (a) TP53 expression in tumor samples. (b) TP53 expression in tumor-normal samples. ∗indicates P<0.05, ∗∗indicates p<0.01, ∗∗∗indicates P<0.001.
(a)(b)To further investigate the difference among tumor-normal samples, we analyzed the UCSC Xena data. In view of the fact that some types of cancers included only a few normal samples (for instance, less than 10 or no normal samples datasets), 16 types of cancer were retained for further analysis (Figure2(b)). Compared with normal samples, we observed higher expression of TP53 in most cancer types, for instance, lung squamous cell carcinoma (LUSC, P=0.0001894), rectum adenocarcinoma (READ, P=0.007547), stomach adenocarcinoma (STAD, P=1.229e−06), colon adenocarcinoma (COAD, P=4.4e−09), kidney renal papillary cell carcinoma (KIRP, P=3.848e−12), uterine corpus endometrial carcinoma (UCEC, P=1.655e−08), prostate adenocarcinoma (PRAD, P=0.0003865), bladder urothelial carcinoma (BLCA, P=0.0321), lung adenocarcinoma (LUAD, P=3.794e−09), liver hepatocellular carcinoma (LIHC, P=5.799e−06), kidney renal clear cell carcinoma (KIRC, P<2.2e−16), thyroid cancer (THCA, P=4.995e−12), and esophageal cancer (ESCA, P=0.01165). In contrast, TP53 expression was downregulated in kidney chromophobe (KICH, P=1.145e−07). Moreover, there were no significant difference in TP53 expression levels in breast invasive cancer (BRCA, P=0.2613) and head and neck squamous cell carcinoma (HNSC, P=0.9583).


## 3.2.TP53 Mutation in Pan-Cancer Cohorts

After eliminating those cancers with the small number samples (for instance, cancer types with less than thirty samples were excluded), 19 cancer types were screened for depicting theTP53 mutation types in pan-cancer, and the landscape was exhibited in Figure 3. Consistent with the previous studies [13, 21], we found that missense mutations accounted for the most of TP53 variants in 19 types of cancer. Next, nonsense mutation ranked second.Figure 3
Landscape of somatic mutations ofTP53 gene in pan-cancer using the UCSC Xena cohort.


## 3.3. Prognostic Impact ofTP53 across TP53-Mut Cancers

In Figure4, forest plots showed that TP53 was high-risk gene in LGG (hazard ratio = 1.614), PRAD (hazard ratio = 11.97), and UCS (hazard ratio = 2.748), while it was a low-risk gene in UCEC (hazard ratio = 0.5717). Furthermore, we carried out prognostic impact of TP53 expression using mean value as criteria and revealed the significant difference between high and low expression groups among LGG (P=0.0084), PRAD (P=0.0035), UCS (P=0.0098), and UCEC (P=0.0068).Figure 4
Association betweenTP53 expression and overall survival time across TP53-mut cancer types. ∗indicates P<0.05, ∗∗indicates P<0.01, ∗∗∗indicates P<0.001.


## 3.4.TP53 Mutation Classification and Survival

We distinguishedTP53 mutations into truncating and nontruncating classes to observe their effects on TP53 expression. In line with the earlier conclusion, the results (see Figure 5) showed that TP53 expression levels were upregulated in truncating mutation relative to nontruncating patients [15].Figure 5
Association betweenTP53 mutation classification and overall survival time in TP53-mut cancers. ∗indicates P<0.05, ∗∗indicates P<0.01, ∗∗∗indicates P<0.001.Furthermore, we performed survival analysis for the sake of the evaluation ofTP53 mutation’s prognostic value. Unfortunately, no significance difference was detected between TP53 mutation types and overall survival time in any type of cancer (Figure 5). However, KM (Kaplan−Meier) results indicated a clear trend that individuals with truncating mutation had longer survival time in specific cancers, such as READ, BLCA, and PAAD.


## 3.5. Correlation betweenTP53 and Mismatch Repair Genes

Mismatch Repair (MMR) is a typical DNA repair mechanism [22]. Ectopic expression of MMR genes might induce the high frequency of somatic mutations [23, 24]. In truncating TP53-mut cancer types (see Figure 6(a)), we examined that MMR genes had significant positive correlation with HNSC (MLH1 correlation coefficient/P-value = 0.27/0.00096; MSH6 correlation coefficient/P-value = 0.17/0.04), LIHC (MSH2 correlation coefficient/P-value = 0.47/0.003; PMS2 correlation coefficient/p-value = 0.33/0.04; MSH6 correlation coefficient/P-value = 0.41/0.0104), and LGG (MLH1 correlation coefficient/P-value = 0.28/0.047; MSH2 correlation coefficient/P-value = 0.31/0.02; MSH6 correlation coefficient/P-value = 0.35/0.012). In contrast, negative correlation with TP53 expression was discovered in four cancers, including COAD (MLH1 correlation coefficient/P-value = −0.42/0.0009), UCEC (MLH1 correlation coefficient/P-value = −0.47/0.0005), SKCM (PMS2 correlation coefficient/P-value = −0.5/0.0097), and STAD (PMS2 correlation coefficient/P-value = −0.25/0.04).Figure 6
Association betweenTP53 expression and MMR genes in 19 TP53-mut cancers. (a) The relationship between TP53 expression and MMR genes in truncating TP53-mut cancers. (b) The relationship between TP53 expression and MMR genes in nontruncating TP53-mut cancers. For each cell, the top left triangle represents the P value and the bottom right triangle represents the correlation coefficient. ∗indicates P<0.05, ∗∗indicates P<0.01, ∗∗∗indicates P<0.001.
(a)(b)In nontruncatingTP53-mut cancer types (Figure 6(b)), we also explored the positive correlation across multiple cancers. Interestingly, compared with different TP53 nontruncating classification, there was opposite correlations in UCEC (MSH6 correlation coefficient/P-value = 0.24/0.005), SKCM (MSH6 correlation coefficient/P-value = 0.37/0.03), and STAD (MSH2 correlation coefficient/P-value = 0.43/3.267e − 05; PMS2 correlation coefficient/P-value = 0.32/0.003; MSH6 correlation coefficient/P-value = 0.38/0.0003).


## 3.6. Correlations betweenTP53 and Immunity

Tumor microenvironment (TME) played an important role during neoplasm occurrence and progression [16, 25, 26]. In this study, we calculate the stromal score, immune score, and estimate score in 19 TP53-mut cancers using the ESTIMATE method. As shown in Table 1, in TP53 truncating mutation cancers, such as BRCA, HNSC, LIHC, LUAD, LUSC, SKCM, PAAD, SARC, and STAD, TP53 was significantly positively correlated with the immune score, as well as stromal score and estimate score, while it was negatively correlated with UCS. Conversely, in nontruncating mutation cancers (for instance, BRCA, GBM, OV, and PRAD), TP53 was significantly negatively associated with the estimated TME scores, while LIHC and PAAD had the opposite consequences.Table 1
Correlation betweenTP53 expression and tumor microenvironment in 19 TP53-mut cancers.
Cancer typesTP53 classificationCor/P-valueStromal scoreImmune scoreEstimate scoreBRCATruncating0.32/0.0003∗∗∗0.3/0.0006∗∗∗0.35/5.607e − 05∗∗∗Nontruncating−0.4/7.994e − 09∗∗∗0.07/0.34−0.2/0.02∗BLCATruncating0.11/0.370.13/0.280.13/0.28Nontruncating−0.05/0.56−0.2/0.07−0.1/0.2COADTruncating0.09/0.510.16/0.220.13/0.31Nontruncating−0.1/0.22−0.12/0.14−0.1/0.16UCECTruncating0.13/0.350.03/0.850.08/0.6Nontruncating−0.1/0.21−0.16/0.053−0.15/0.07ESCATruncating0.08/0.58−0.08/0.590.0009/0.995Nontruncating−0.01/0.92−0.21/0.06−0.11/0.31GBMTruncating0.07/0.840.16/0.640.13/0.71Nontruncating−0.3/0.07−0.32/0.053−0.33/0.04∗HNSCTruncating0.3/0.0002∗∗∗0.3/0.0002∗∗∗0.32/7.295e − 05∗∗∗Nontruncating0.1/0.180.04/0.560.08/0.29LIHCTruncating0.45/0.004∗∗0.42/0.007∗∗0.47/0.002∗∗Nontruncating0.15/0.210.24/0.046∗0.22/0.07LGGTruncating0.14/0.32−0.14/0.32−0.01/0.94Nontruncating−0.09/0.220.03/0.72−0.02/0.81LUADTruncating0.27/0.009∗∗0.3/0.005∗∗0.31/0.003∗∗Nontruncating−0.03/0.72−0.09/0.3−0.07/0.43LUSCTruncating0.28/0.002∗∗0.25/0.004∗∗0.28/0.0014∗∗Nontruncating−0.05/0.4−0.07/0.29−0.07/0.31SKCMTruncating0.7/6.962e − 05∗∗∗0.8/1.285e − 06∗∗∗0.81/5.301e − 07∗∗∗Nontruncating−0.09/0.62−0.15/0.39−0.13/0.45OVTruncating0.15/0.16−0.006/0.950.08/0.47Nontruncating−0.17/0.044∗−0.13/0.11−0.16/0.054PAADTruncating0.32/0.090.47/0.0105∗0.43/0.02∗Nontruncating0.23/0.110.4/0.004∗∗0.34/0.02∗PRADTruncating−0.08/0.750.08/0.76−0.005/0.98Nontruncating−0.36/0.04∗−0.24/0.18−0.32/0.07READTruncating0.36/0.060.26/0.180.34/0.08Nontruncating−0.08/0.530.05/0.68−0.02/0.87SARCTruncating0.52/0.002∗∗0.24/0.170.4/0.02∗Nontruncating0.08/0.560.008/0.960.04/0.8STADTruncating0.46/0.0001∗∗∗0.27/0.03∗0.4/0.0008∗∗∗Nontruncating−0.01/0.92−0.06/0.61−0.04/0.74UCSTruncating−0.61/0.04∗−0.13/0.7−0.43/0.17Nontruncating−0.07/0.70.2/0.240.09/0.62∗indicates P<0.05; ∗∗indicates P<0.01; ∗∗∗indicates P<0.001.Subsequently, we investigated the immune cell infiltration levels amongTP53-mut caners. The results implied the diverse significant correlation between levels of immune cell infiltration and TP53 expression among TP53 truncating or nontruncating mutant cancers. Significant correlation is screened and presented in Table 2.Table 2
Significant correlation betweenTP53 expression and immune cell infiltration in 19 TP53-mut cancers.
Cell typesTP53 classification in 19 TP53-mut cancers (cor/P-value)TruncatingNon-truncatingNaive B cellsUCS: 0.65/0.02∗BLCA: 0.22/0.02∗LGG: −0.23/0.002∗∗LUAD: 0.18/0.03∗Memory B cellsPAAD: 0.38/0.045∗NAPlasma cellsNALGG: −0.17/0.02∗CD8 T cellsUCEC: 0.41/0.003∗∗BRCA: 0.22/0.002∗∗SKCM: 0.45/0.02∗LIHC: 0.25/0.04∗LUAD: 0.18/0.04∗LUSC: 0.16/0.012∗Naive CD4 T cellsHNSC: −0.2/0.04∗LGG: −0.18/0.02∗OV: 0.37/0.0002∗∗∗READ: −0.3/0.02∗Resting CD4 memory T cellsUCEC: −0.37/0.007∗∗BRCA: −0.2/0.007∗∗PAAD: −0.4/0.009∗∗PRAD: −0.4/0.04∗Activated CD4 memory T cellsNABRCA: 0.3/1.872e − 05∗∗∗GBM: −0.4/0.02∗Follicular T helper cellsUCEC: 0.34/0.014∗BRCA: 0.3/1.876e − 05∗∗∗OV: −0.24/0.02∗LGG: 0.16/0.03∗STAD: 0.3/0.013∗Regulatory T cellsLUSC: 0.22/0.011∗LUSC: 0.14/0.03∗SKCM: 0.44/0.03∗Gamma delta T cellsOV: 0.36/0.0003∗∗∗GBM: −0.33/0.04∗LUAD: −0.22/0.009∗∗PAAD: 0.65/3.765e − 07∗∗∗PRAD: 0.4/0.02∗Resting NK cellsBLCA: 0.31/0.013∗BRCA: −0.2/0.02∗SKCM: −0.4/0.04∗READ: −0.3/0.03∗OV: 0.34/0.0007∗∗∗SARC: 0.36/0.011∗Activated NK cellsNACOAD: 0.22/0.008∗∗UCEC: −0.18/0.03∗GBM: 0.49/0.002∗∗LGG: 0.23/0.002∗∗PRAD: 0.46/0.007∗∗M0 macrophagesESCA: 0.4/0.004∗∗UCEC: 0.21/0.014∗LUSC: 0.22/0.02∗OV: 0.25/0.002∗∗OV: 0.24/0.02∗M1 macrophagesNABRCA: 0.14/0.045∗BLCA: −0.2/0.047∗PAAD: 0.3/0.03∗PRAD: −0.43/0.013∗M2 macrophagesBRCA: −0.25/0.005∗∗BRCA: −0.4/8.285e − 09∗∗∗LGG: −0.31/0.03∗HNSC: 0.21/0.006∗∗UCS: −0.6/0.046∗LUAD: −0.2/0.04∗Resting dendritic cellsSKCM: 0.39/0.047∗NAActivated dendritic cellsBRCA: −0.23/0.008∗∗BRCA: 0.15/0.04∗OV: −0.2/0.03∗HNSC: −0.2/0.014∗PRAD: 0.6/0.008∗∗Resting mast cellsCOAD: 0.4/0.002∗∗BRCA: −0.4/3.896e − 08∗∗∗LIHC: −0.25/0.04∗SARC: −0.35/0.014∗Activated mast cellsHNSC: −0.2/0.004∗∗BRCA: 0.21/0.003∗∗LIHC: 0.37/0.02∗HNSC: −0.3/0.0001∗∗∗LUAD: −0.2/0.03∗LUSC: −0.1/0.047∗UCS: 0.44/0.008∗∗EosinophilsNABRCA: 0.14/0.04∗COAD: −0.2/0.04∗LGG: 0.18/0.02∗SKCM: −0.5/0.004∗∗NeutrophilsHNSC: −0.2/0.02∗COAD: −0.22/0.006∗∗STAD: −0.26/0.03∗HNSC: −0.2/0.008∗∗LUAD: −0.2/0.012∗LUSC: −0.22/0.0006∗∗∗MonocytesNACOAD: −0.2/0.04∗GBM: −0.36/0.03∗OV: −0.25/0.002∗∗STAD: −0.27/0.011∗∗indicates P<0.05; ∗∗indicates P<0.01; ∗∗∗indicates P<0.001. NA indicates no statistical correlation.


## 3.7. Correlation betweenTP53 and Biological Function

We carried out a thorough inspection of Gene Set Enrichment Analysis (GSEA) to investigate the relationship betweenTP53 and biological function inTP53-mut tumor tissues, and the results are shown in Supplementary Figures (Available here).InTP53 truncating vs nontruncating mutation cancers, the KEGG data indicated that TP53 positively regulated RIG-I-like receptor signal pathway [27] and cytosolic DNA-sensing pathway [28, 29] in BLCA and OV. In contrast, TP53 was predicted to be a negative regulator of the T cell receptor signaling pathway [30], cytosolic DNA-sensing pathway and RIG-I-like receptor signal pathway in GBM, and/or LUSC (Figure 7). In GBM and UCS, the GO data showed that TP53 expression was negatively correlated with adaptive immune response, immune response − regulating signal pathway and had a positive regulation with immune response. In READ, TP53 expression exhibited the opposite effect (see Figure 8).Figure 7
KEGG analysis ofTP53 in BLCA, OV, GBM, and LUSC. Peaks on the upward curve indicate positive regulation and peaks on the download curve indicate negative regulation.Figure 8
GO analysis ofTP53 in READ, GBM, and UCS. Peaks on the upward curve indicate positive regulation and peaks on the download curve indicate negative regulation.


## 4. Discussion

In this article, the expression level ofTP53 gene was diverse in tumor or normal tissues. TP53 expression was higher in most cancers than in normal tissues except KICH.Besides, we analyzed the variation ofTP53 in 19 cancers and discovered that missense mutation was the dominant subtype, which is consistent with the previous conclusion [13]. At the same time, the evidences showed that patients with the higher expression of TP53 had a worse survival in UCS, LGG, and PRAD. On the contrary, in UCEC, the higher expression of TP53 gene was linked to better survival.In previous studies, the prognosis roles ofTP53 mutation were controversial [15]. Meanwhile, the expression level of TP53 is often related to the mutation types [13]. In order to examine the impact of TP53 mutations on prognosis, TP53 mutations were divided into truncating and nontruncating mutation groups referring to the published reliable classification method [13]. Our results demonstrated that the patients with truncating mutations presented lower TP53 expression. Besides, Kaplan−Meier analysis showed a clear trend that individuals with truncating TP53 mutation had longer survival time in BLCA (P-value = 0.12), PAAD (P-value = 0.24), and READ (P-value = 0.12), consistent with the results of previous published literature [13].Dan et al. [22] clarified that the abnormal expression of mismatch repaired genes induced the increased frequency of somatic mutation. In colorectal cancer, Perez et al. [31] indicated that mismatch-repaired deficiency can induce TP53 mutation. Fang et al. [32] reported that TP53 defection and mismatch-repaired deficiency commonly occurred in early carcinosarcoma. The correlation between TP53 mutation classification and the expression of the MMR genes was analyzed. In the truncating mutation group, TP53 expression is positively correlated with MMR genes expression in LGG, LIHC, and HNSC, while negative correlation was found in other four cancers, including COAD, SKCM, UCEC, and STAD. In contrast, in the nontruncating mutation group, we detected the positive correlation in most cancers. Compared to TP53 nontruncating classification, there were opposite correlations in UCEC, SKCM, and STAD.We further explored the relationship betweenTP53 mutation classification and tumor microenvironment. According to the ESTIMATE algorithm [18, 19], we calculated the stromal score, immune score, and estimate score. In the TP53 truncating mutation group, TP53 is significantly positively correlated with immune score, as well as stromal score and estimate score in specific cancers, such as BRCA, HNSC, LIHC, LUAD, LUSC, SKCM, PAAD, SARC, and STAD. Whereas, it is negatively correlated in UCS. Conversely, in the nontruncating mutation group, TP53 is significantly negatively correlated with stromal/estimate/immune score in BRCA, GBM, OV, and PRAD, while LIHC and PAAD had opposite results.Correlation between the degree of immune cell infiltration andTP53 expression was estimated further. In truncating or nontruncating mutation samples, obvious associations between the previous two factors were shown in most cancers. Finally, GSEA results indicated that TP53 (truncating or nontruncating) was involved in some immune−related function and pathways.Some limitations of this paper include the limited databases and scarce experimental verification. In the future, we will adopt more valuable databases and experimental results to confirm and improve our work.


## 5. Conclusions

In conclusion, this might be the first comprehensive and systematic research to evaluate the immune − related mechanisms ofTP53 mutation classification in different cancer. According to our results, TP53 is related to immunological function based on different mutation classification in various cancers. It is worth mentioning that these findings might extend better understanding of TP53 gene underlying the mechanism in the immune system.



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*Source: 1904309-2023-02-13.xml*