# A Pan-Cancer Analysis of the Role of PBRM1 in Human Tumors

Authors: Jin Liu; Xiaoli Xie; Min Xue; Jianqing Wang; Qian Chen; Zhen Zhao; Xia Sheng
Journal: Stem Cells International
 (2022)

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## Abstract

To understand common but distinctive systems to drive oncogenic stages in human tumors is critical for understanding disease programme and developing novel therapeutic strategies. PBRM1 is a critical gene in oncogenesis. We found that PBRM1 is upregulated in multiple cancer genes. Prognostic analyses indicated that higher PBRM1 showed better disease outcomes of head and neck squamous cell carcinoma (HNSC), KIRC, and UCEC, while poorer outcomes in KICH, skin cutaneous melanoma (SKCM), and esophageal carcinoma (ESCA). PBRM1 mutation was most frequent in renal cell carcinoma and showed better disease outcomes of pan-cancer. We also discovered that PBRM1 performance was associated with endothelial cell invasion status in COAD, HNSC, KIRC, LUAD, LUSC, OV, and PAAD, and cancer-related fibroblast invasion was observed in COAD, HNSC, KIRC, LUSC, MESO, OV, and PAAD. We also make the comparison of PBRM1’s phosphorylation between normal and basic tumor systems as well as explore potential systems with distinctive functions in PBRM1-mediated oncogenesis. The analysis of pan-cancer offers us an outline of PBRM1’s functions in various human cancers, which could promote a comprehensive understanding of PBRM1 in tumorigenesis.


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## Body

## 1. Introduction

Cancer, a disease, marks the largest morbidity and death rate throughout the world at present [1]. In these years, the analysis of cancer pathogenesis has become the center in scientific research. Although the distribution of cancers in different tissues and organs results in some heterogeneity, the initiation and progression of many cancers share certain common rules. Therefore, analyzing the pan-cancer expression and clinical prognosis of key genes in tumor progression is crucial in exploring their underlying molecular systems [1]. There are many pan-cancer studies on critical oncogenes during these years, like HER2, DLGAP5, SND1, and ARID1A. Evaluating the roles of other critical genes could help us better understand the initiation and development of cancers [2–4].PBRM1 (BAF180) is a subgroup of the ATP-dependent chromatin-remodeling group [5]. Elements of the SWI/SNF complex are transformed into several cancers, and PBRM1 was shown to wield tumor suppressor function in breast cancer [5–7]. Previous studies showed that PBRM1 could act as a central element of complexes which is essential for ligand-dependent transcriptional activation of nuclear hormone receptors [8]. Previous studies showed that loss of PBRM1 performance might contribute to the progression of renal cell carcinoma, and its expression level correlates with disease prognosis [9–11]. However, the role of PBRM1 remains elusive in other tumor types.In this research, we utilized remote tools to test the latest pan-cancer RNA sequencing (RNA-Seq) data of TCGA and other online datasets to determine PBRM1 expression patterns and associated prognosis in various cancer types. RNA-Seq transcriptome sequencing technology is to use high-throughput sequencing technology sequencing analysis, reflecting the mRNA, small RNA, noncoding RNA, or some such as expression level. RNA-Seq technology has evolved rapidly over the past decade and has become an indispensable tool for the analysis of differential gene expression/mRNA variable shear at the transcriptome level. We also identified coexpressed genes to explore potential mechanisms. Our findings can be used to uncover the obscure function of PBRM1 in multiple tumor types, thereby revealing the potential molecular systems in PBRM1 for the pathogenesis and clinical prognosis in various human cancers.


## 2. Materials and Related Methods

### 2.1. Genetic Expression Research

We utilized the TIMER2 (Cancer Immunoevaluation Resources, Version 2) website (http://timer.cistr-ome.org/) for obtaining PBRM1 expressive patterns between tumors and normal systems of different tumors from The Cancer Genome Atlas (TCGA). The benefits of TIMER2 are clear because it has three major functions.
(1)
Immune association: the immune component contains four modules that allow users to investigate the relationship between estimated immune infiltration and gene expression, somatic mutations, somatic copy number alterations (sCNAs), and clinical outcomes in TCGA cohorts(2)
Cancer exploration: there are four modules that allow users to search for cancer-related associations in TCGA. Users can compare the expression of a gene between tumor and normal and find out how the expression of one gene is related to patient survival or the mutation status and expression level of other genes. Input for each module in the immune and exploration components presents a functional heat chart showing the association between each input feature and the type of cancer(3)
Immune estimation: based on the user-provided expression data, the estimation component uses six estimation algorithms to infer immune cell infiltrationTIMER2.0 provides instructions for each module on the module page and an overview of the entire site with tutorial videos on the home page. Researchers also used the GEPIA2 (Genetic Expression Profile Interaction Analysis, 2nd Edition) tool (http://gepia2.cancer-pku.cn/#analysis) to get the block diagram of GTEx (Genotype System Expression) database [12]. Use UALCAN tool CPTAC clinical proteomic analysis of tumor (alliance) datasets, and compare the primary tissue and normal system in PBRM1 phosphorus total protein or protein expression level (http://ualcan.path.uab.edu/analysisprot.html) [13].


### 2.2. Disease Prognosis Analysis

In this study, we obtained overall survival (OS) and disease-free survival (DFS) using GEPIA2 to analyze numbers and survival parts in PBRM1 throughout different cancer types. High (50%) and low (50%) truncation values were used as expression thresholds to separate high and low expression queues [14]. The clinical outcome was calculated by the Kaplan–Meier method with the log-rank test.


### 2.3. Gene Alterative Study

Data across all cancer types on mutation frequency, mutation type, and mutation site information were collected in the online cBioPortal website (https://www.cbioportal.org/).


### 2.4. Immunological Invasion Study

The relationship of PBRM1 performance and immunological cell invasion across cancer was analyzed by utilizing the TIMER2 tool. Cancer-related fibroblast and endothelial cells were chosen to do a specific study. The TIMER, TIDE, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER, and EPIC algorithms were used to estimate.


### 2.5. Identification of PBRM1-Associated Genetic Enriching Study

Researchers got the highest 200 PBRM1-related genes based on the data of all TCGA tumor systems by utilizing the GEPIA2 online tool. We then did paired-gene Pearson correlative between PBRM1 and the chosen genes and calculatedP value and correlation coefficient (R) directly online.


### 2.6. Functional Illustration and Method Enrichment Study

We did the functional genetic study using the dataset for annotation, visualization, and integrated discovery (DAVID) website (https://david.ncifcrf.gov/), a necessary instruction for functional gene analysis [15]. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) researches were launched by specifying a P value < 0.05 with number importance.


## 2.1. Genetic Expression Research

We utilized the TIMER2 (Cancer Immunoevaluation Resources, Version 2) website (http://timer.cistr-ome.org/) for obtaining PBRM1 expressive patterns between tumors and normal systems of different tumors from The Cancer Genome Atlas (TCGA). The benefits of TIMER2 are clear because it has three major functions.
(1)
Immune association: the immune component contains four modules that allow users to investigate the relationship between estimated immune infiltration and gene expression, somatic mutations, somatic copy number alterations (sCNAs), and clinical outcomes in TCGA cohorts(2)
Cancer exploration: there are four modules that allow users to search for cancer-related associations in TCGA. Users can compare the expression of a gene between tumor and normal and find out how the expression of one gene is related to patient survival or the mutation status and expression level of other genes. Input for each module in the immune and exploration components presents a functional heat chart showing the association between each input feature and the type of cancer(3)
Immune estimation: based on the user-provided expression data, the estimation component uses six estimation algorithms to infer immune cell infiltrationTIMER2.0 provides instructions for each module on the module page and an overview of the entire site with tutorial videos on the home page. Researchers also used the GEPIA2 (Genetic Expression Profile Interaction Analysis, 2nd Edition) tool (http://gepia2.cancer-pku.cn/#analysis) to get the block diagram of GTEx (Genotype System Expression) database [12]. Use UALCAN tool CPTAC clinical proteomic analysis of tumor (alliance) datasets, and compare the primary tissue and normal system in PBRM1 phosphorus total protein or protein expression level (http://ualcan.path.uab.edu/analysisprot.html) [13].


## 2.2. Disease Prognosis Analysis

In this study, we obtained overall survival (OS) and disease-free survival (DFS) using GEPIA2 to analyze numbers and survival parts in PBRM1 throughout different cancer types. High (50%) and low (50%) truncation values were used as expression thresholds to separate high and low expression queues [14]. The clinical outcome was calculated by the Kaplan–Meier method with the log-rank test.


## 2.3. Gene Alterative Study

Data across all cancer types on mutation frequency, mutation type, and mutation site information were collected in the online cBioPortal website (https://www.cbioportal.org/).


## 2.4. Immunological Invasion Study

The relationship of PBRM1 performance and immunological cell invasion across cancer was analyzed by utilizing the TIMER2 tool. Cancer-related fibroblast and endothelial cells were chosen to do a specific study. The TIMER, TIDE, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER, and EPIC algorithms were used to estimate.


## 2.5. Identification of PBRM1-Associated Genetic Enriching Study

Researchers got the highest 200 PBRM1-related genes based on the data of all TCGA tumor systems by utilizing the GEPIA2 online tool. We then did paired-gene Pearson correlative between PBRM1 and the chosen genes and calculatedP value and correlation coefficient (R) directly online.


## 2.6. Functional Illustration and Method Enrichment Study

We did the functional genetic study using the dataset for annotation, visualization, and integrated discovery (DAVID) website (https://david.ncifcrf.gov/), a necessary instruction for functional gene analysis [15]. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) researches were launched by specifying a P value < 0.05 with number importance.


## 3. Results

### 3.1. Genetic Expressive Study

In this research, we tried to comprehensively analyze PBRM1 in pan-cancer. First, we obtained distinct PBRM1 performance among tumors and normal systems in distinctive tumor types of TCGA projects. Researchers used TIMER2 (only TCGA data) and GEPIA2 (data combined TCGA and GTEx) online tools to get parts of the expressive level of tumor and normal systems. As shown in Figures1(a) and 1(b), PBRM1 was upregulated in cholangiocarcinoma (CHOL), liver hepatocellular carcinoma (LIHC), stomach adenocarcinoma (STAD), lymphoid neoplasm diffuse large B cell lymphoma (DLBC), acute myeloid leukemia (LAML), glioblastoma multiforme (GBM), brain’s lower grade glioma (LGG), pancreatic adenocarcinoma (PAAD), ovarian serous cystadenocarcinoma (OV), and thymoma (THYM). Meanwhile, PBRM1 was down in breast cancer (BRCA), urothelial bladder carcinoma (BLCA), colon adenocarcinoma (COAD), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC).Figure 1
Expressive level and pathological stages of PBRM1 gene in various tumors. (a) The expressive figures of the PBRM1 gene in various cancers or targeted cancer subtypes;∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001. (b) In TCGA project, the expressive status of PBRM1 in DLBC, LAML, LGG, and thymus was compared to the related normal systems in GTEx data. ∗P<0.05. (c) Based on the CPTAC dataset, researchers also studied the expressive level of PBRM1 total protein between normal tissue and primary tissue of RCC, UCEC, COAD, OV, LIHC, GBM, PAAD, and LUAD. (d) According to TCGA data, PBRM1 gene expression level was analyzed according to the main pathological stages of KIRC and OV (stages I, II, III, and IV). Log2 (TPM+1) is used for the logarithmic scale.
(a)(b)(c)(d)Then, we utilized UALCAN remote instruction to evaluate the total protein expressive level of PBRM1 in various cancers using data from the CPTAC dataset. We found that PBRM1 protein was expressed lower in RCC and higher in UCEC, COAD, OV, BRCA, LIHC, GBM, PAAD, and LUAD (Figure1(c)).Moreover, we used GEPIA2 to unveil the relation between PBRM1 expressive level and the uncontrollable process of various tumors. The data indicated that the PBRM1 expressive standard is strongly related to the pathological stages in KIRC and OV (Figure1(d)) but not in others.


### 3.2. The Data of the Current Analytical Result

Next, we focused on whether PBRM1 expression criteria were associated with disease outcome. According to the expression level of PBRM1, these cases were divided into the high expression group and the low expression group. We then used TCGA data via GEPIA2 to examine the relationship between PBRM1 performance and patient predictions for various tumors. The results showed that high expression of PBRM1 was associated with worse OS (overall survival) prediction of KICH or SKCM and better prediction of HNSC and KIRC (Figure2(a)). In the DFS (disease-free survival) analysis, we found that better PBRM1 performance was associated with better UCEC prediction for ACC, ESCA, and KICH and worse prognosis (Figure 2(b)).Figure 2
The relationship between PBRM1 expression level and survival rate in TCGA tumors. GEPIA2 was used to assess the association between PBRM1 gene expression and (a) overall survival and (b) disease-free survival in all TCGA tumors. Positive results for survival plots and Kaplan–Meier curves are listed.
(a)(b)


### 3.3. Genetic Alteration Analysis Data

Genetic alteration is common in human cancers and always contributes to cancer development. Then, we explored PBRM1 genetic alteration in multiple human cancer samples and found that the frequency of PBRM1 alteration in renal cell carcinoma is the highest tumors. Bladder cancer also has a higher frequency of PBRM1 alteration (>10%, Figure3(a)). Considering data (Figure 3(b)), researchers showed extra transformation and their position in PBRM1. Then, researchers explored if there was a relation between gene alternatives in PBRM1 and the clinical prediction of patients. Researchers did the analysis in pan-cancer and renal cell carcinoma, which has the highest frequency of PBRM1 alteration. We found that in pan-cancer, patients with PBRM1 mutation showed better outcomes in OS, while renal cell carcinoma patients with gene alternatives in PBRM1 illustrated no significant correlation with better or poor prognosis in OS compared with patients without PBRM1 alterations (Figures 3(c) and 3(d)).Figure 3
Transformation characteristics in PBRM1 in various tumors of TCGA. (a) Transformation frequency in PBRM1 in pan-cancer. (b) The change frequency of PBRM1 mutation types and mutation sites in TCGA tumors was demonstrated by cBioPortal tool. (c, d) Potential correlation between transformation status and overall survival of pan-cancer and renal cell cancer conducted by the cBioPortal tool.
(a)(b)(c)(d)


### 3.4. Protein Phosphorylation Analytical Data

It is well known that the phosphorylation-dephosphorylation cascade is critical in tumorigenesis. PBRM1 is undergoing several posttranslational modifications, including phosphorylation of tyrosine, serine, and threonine residues. Therefore, we compared the phosphorylation of PBRM1 in normal and primary tumor tissues. We analyzed all cancer types using the CPTAC dataset. Researchers found lower levels of phosphorylation in the Y372 and S374 and a higher level of phosphorylation in S60 to breast cancer (Figure4(a)). We discovered lower level of phosphorylation in S1023 to ccRCC (Figure 4(b)). Phosphorylation levels of S519 and S526 in LUAD and phosphorylation levels of T525 and S526 LIHC were all higher (Figures 4(c) and 4(d)). Finally, we found a lower phosphorylation level of S60 and S374 and a higher phosphorylation level of S152, T322, S519, and S1023 in HNSC (Figure 4(e)).Figure 4
Tumor-related protein phosphorylation in PBRM1. Comparison of the level of PBRM1 proteins (Y372, S374, S60, S519, S526, T525, S152, T322, and S1023) among normal systems and basic systems of chosen tumors. (a) Block diagram representation of PBRM1 protein levels in breast cancer. (b) Block diagram representation of PBRM1 protein levels in clear cell RCC. (c) Block diagram representation of PBRM1 protein levels in LUAD. (d) Block diagram representation of PBRM1 phosphoprotein levels in LICH. (e) Block diagram representation of PBRM1 protein levels in HNSC.
(a)(b)(c)(d)(e)


### 3.5. Immune Infiltration Analysis Data

In the next step, we investigated whether changes in PBRM1 expression level could affect tumor-invading immune responses. We used TIMER, CIBERSORT, CIBERSORT-ABS, TIDE, XCELL, MCPCOUNTER, QUANTISEQ, and EPIC algorithms to investigate the relation between the levels of infiltration of distinctive immune cells and endothelial cells and the expression of PBRM1 in various tumor types of TCGA. After analyzing the results, we found that PBRM1 expression of COAD, HNSC, KIRC, LUAD, LUSC, OV, and PAAD was positively correlated with endothelial cell infiltration (Figure5(a)). We also found that PBRM1 expression was positively correlated with cancer-associated fibroblast infiltration to COAD, HNSC, KIRC, LUSC, MESO, OV, and PAAD (Figure 5(b)).Figure 5
The relation among PBRM1 expressive level and invasion of endothelial cells and cancer-related fibroblasts. (a) TIMER, CIBERSORT, CIBERSORT-ABS, TIDE, XCELL, MCPCOUNTER, QUANTISEQ, and EPIC algorithms were used to analyze the correlation between endothelial cell level and PBRM1 gene expression level of all tumors in TCGA. (b) TIMER, CIBERSORT, CIBERSORT-ABS, TIDE, XCELL, MCPCOUNTER, QUANTISEQ, and EPIC algorithms were utilized to analyze cancer-related fibroblast invasion levels and PBRM1 gene expressive levels in all tumors in TCGA. Red represents a positive correlation (0–1), while blue indicates an opposite correlation (−1–0). The relation withP<0.05 was statistically significant. Statistically insignificant correlation values are signed with a cross.
(a)(b)


### 3.6. Identification of PBRM1-Related Genes and Functional Enrichment Analysis

To further investigate the critical pathways and genes correlated with PBRM1 expression in pan-cancer, we identified the top 200 PBRM1-related genes using GEPIA2. Then, we utilized the STRING instruction to acquire the interactive net of these PBRM1-related genes (Figure6(a)). We also showed the top 5 related genes. The expression of PBRM1 was not negatively associated to that in RAD54L2 (0.8), DENND6A (0.8), DCP1A (0.8), APPL1 (0.82), and SETD2 (0.84) genes (all <0.001) (Figures 6(b)–6(f)).Figure 6
PBRM1-associated genetic expansion and pathway study. (a) Tandem protein network diagram of PBRM1 binding protein determined experimentally. Colored nodes represent individual proteins that have been identified. (b–f) Expressive correlation among PBRM1 and typical genes (RAD54L2, DENND6A, DCP1A, APPL1, and SETD2) of the greatest PBRM1-related genes in TCGA projects determined by GEPIA2. (g, h) GO and KEGG pathway study according to the PBRM1-correlated genes.
(a)(b)(c)(d)(e)(f)(g)(h)In order to analyze the above-related genes at the basic level, we uploaded 300 DEGs online for greater GO and KEGG pathway analysis with DAVID separately. GO analysis about DEGs showed transcriptional regulation of RNA polymerase II promoter, positive transcriptional regulation of RNA polymerase II promoter, positive transcriptional regulation, DNA templatization, chromatin remodeling, transcriptional regulation, DNA templarization, mRNA processing, viral process, mRNA destabilization, protein phosphorylation, regulation of megakaryocyte differentiation, cell division, protein-dependent catabolic process, cell cycle, peptidyl-serine phosphorylation, and ATP-dependent chromatin remodeling (Figure6(g)).Moreover, in KEGG analysis, the genes above were obviously increased in lysine degradation, RNA degradation, protein middle proteolysis, TGF-beta signaling method, and hepatocellular carcinoma (Figure6(h)).


## 3.1. Genetic Expressive Study

In this research, we tried to comprehensively analyze PBRM1 in pan-cancer. First, we obtained distinct PBRM1 performance among tumors and normal systems in distinctive tumor types of TCGA projects. Researchers used TIMER2 (only TCGA data) and GEPIA2 (data combined TCGA and GTEx) online tools to get parts of the expressive level of tumor and normal systems. As shown in Figures1(a) and 1(b), PBRM1 was upregulated in cholangiocarcinoma (CHOL), liver hepatocellular carcinoma (LIHC), stomach adenocarcinoma (STAD), lymphoid neoplasm diffuse large B cell lymphoma (DLBC), acute myeloid leukemia (LAML), glioblastoma multiforme (GBM), brain’s lower grade glioma (LGG), pancreatic adenocarcinoma (PAAD), ovarian serous cystadenocarcinoma (OV), and thymoma (THYM). Meanwhile, PBRM1 was down in breast cancer (BRCA), urothelial bladder carcinoma (BLCA), colon adenocarcinoma (COAD), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC).Figure 1
Expressive level and pathological stages of PBRM1 gene in various tumors. (a) The expressive figures of the PBRM1 gene in various cancers or targeted cancer subtypes;∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001. (b) In TCGA project, the expressive status of PBRM1 in DLBC, LAML, LGG, and thymus was compared to the related normal systems in GTEx data. ∗P<0.05. (c) Based on the CPTAC dataset, researchers also studied the expressive level of PBRM1 total protein between normal tissue and primary tissue of RCC, UCEC, COAD, OV, LIHC, GBM, PAAD, and LUAD. (d) According to TCGA data, PBRM1 gene expression level was analyzed according to the main pathological stages of KIRC and OV (stages I, II, III, and IV). Log2 (TPM+1) is used for the logarithmic scale.
(a)(b)(c)(d)Then, we utilized UALCAN remote instruction to evaluate the total protein expressive level of PBRM1 in various cancers using data from the CPTAC dataset. We found that PBRM1 protein was expressed lower in RCC and higher in UCEC, COAD, OV, BRCA, LIHC, GBM, PAAD, and LUAD (Figure1(c)).Moreover, we used GEPIA2 to unveil the relation between PBRM1 expressive level and the uncontrollable process of various tumors. The data indicated that the PBRM1 expressive standard is strongly related to the pathological stages in KIRC and OV (Figure1(d)) but not in others.


## 3.2. The Data of the Current Analytical Result

Next, we focused on whether PBRM1 expression criteria were associated with disease outcome. According to the expression level of PBRM1, these cases were divided into the high expression group and the low expression group. We then used TCGA data via GEPIA2 to examine the relationship between PBRM1 performance and patient predictions for various tumors. The results showed that high expression of PBRM1 was associated with worse OS (overall survival) prediction of KICH or SKCM and better prediction of HNSC and KIRC (Figure2(a)). In the DFS (disease-free survival) analysis, we found that better PBRM1 performance was associated with better UCEC prediction for ACC, ESCA, and KICH and worse prognosis (Figure 2(b)).Figure 2
The relationship between PBRM1 expression level and survival rate in TCGA tumors. GEPIA2 was used to assess the association between PBRM1 gene expression and (a) overall survival and (b) disease-free survival in all TCGA tumors. Positive results for survival plots and Kaplan–Meier curves are listed.
(a)(b)


## 3.3. Genetic Alteration Analysis Data

Genetic alteration is common in human cancers and always contributes to cancer development. Then, we explored PBRM1 genetic alteration in multiple human cancer samples and found that the frequency of PBRM1 alteration in renal cell carcinoma is the highest tumors. Bladder cancer also has a higher frequency of PBRM1 alteration (>10%, Figure3(a)). Considering data (Figure 3(b)), researchers showed extra transformation and their position in PBRM1. Then, researchers explored if there was a relation between gene alternatives in PBRM1 and the clinical prediction of patients. Researchers did the analysis in pan-cancer and renal cell carcinoma, which has the highest frequency of PBRM1 alteration. We found that in pan-cancer, patients with PBRM1 mutation showed better outcomes in OS, while renal cell carcinoma patients with gene alternatives in PBRM1 illustrated no significant correlation with better or poor prognosis in OS compared with patients without PBRM1 alterations (Figures 3(c) and 3(d)).Figure 3
Transformation characteristics in PBRM1 in various tumors of TCGA. (a) Transformation frequency in PBRM1 in pan-cancer. (b) The change frequency of PBRM1 mutation types and mutation sites in TCGA tumors was demonstrated by cBioPortal tool. (c, d) Potential correlation between transformation status and overall survival of pan-cancer and renal cell cancer conducted by the cBioPortal tool.
(a)(b)(c)(d)


## 3.4. Protein Phosphorylation Analytical Data

It is well known that the phosphorylation-dephosphorylation cascade is critical in tumorigenesis. PBRM1 is undergoing several posttranslational modifications, including phosphorylation of tyrosine, serine, and threonine residues. Therefore, we compared the phosphorylation of PBRM1 in normal and primary tumor tissues. We analyzed all cancer types using the CPTAC dataset. Researchers found lower levels of phosphorylation in the Y372 and S374 and a higher level of phosphorylation in S60 to breast cancer (Figure4(a)). We discovered lower level of phosphorylation in S1023 to ccRCC (Figure 4(b)). Phosphorylation levels of S519 and S526 in LUAD and phosphorylation levels of T525 and S526 LIHC were all higher (Figures 4(c) and 4(d)). Finally, we found a lower phosphorylation level of S60 and S374 and a higher phosphorylation level of S152, T322, S519, and S1023 in HNSC (Figure 4(e)).Figure 4
Tumor-related protein phosphorylation in PBRM1. Comparison of the level of PBRM1 proteins (Y372, S374, S60, S519, S526, T525, S152, T322, and S1023) among normal systems and basic systems of chosen tumors. (a) Block diagram representation of PBRM1 protein levels in breast cancer. (b) Block diagram representation of PBRM1 protein levels in clear cell RCC. (c) Block diagram representation of PBRM1 protein levels in LUAD. (d) Block diagram representation of PBRM1 phosphoprotein levels in LICH. (e) Block diagram representation of PBRM1 protein levels in HNSC.
(a)(b)(c)(d)(e)


## 3.5. Immune Infiltration Analysis Data

In the next step, we investigated whether changes in PBRM1 expression level could affect tumor-invading immune responses. We used TIMER, CIBERSORT, CIBERSORT-ABS, TIDE, XCELL, MCPCOUNTER, QUANTISEQ, and EPIC algorithms to investigate the relation between the levels of infiltration of distinctive immune cells and endothelial cells and the expression of PBRM1 in various tumor types of TCGA. After analyzing the results, we found that PBRM1 expression of COAD, HNSC, KIRC, LUAD, LUSC, OV, and PAAD was positively correlated with endothelial cell infiltration (Figure5(a)). We also found that PBRM1 expression was positively correlated with cancer-associated fibroblast infiltration to COAD, HNSC, KIRC, LUSC, MESO, OV, and PAAD (Figure 5(b)).Figure 5
The relation among PBRM1 expressive level and invasion of endothelial cells and cancer-related fibroblasts. (a) TIMER, CIBERSORT, CIBERSORT-ABS, TIDE, XCELL, MCPCOUNTER, QUANTISEQ, and EPIC algorithms were used to analyze the correlation between endothelial cell level and PBRM1 gene expression level of all tumors in TCGA. (b) TIMER, CIBERSORT, CIBERSORT-ABS, TIDE, XCELL, MCPCOUNTER, QUANTISEQ, and EPIC algorithms were utilized to analyze cancer-related fibroblast invasion levels and PBRM1 gene expressive levels in all tumors in TCGA. Red represents a positive correlation (0–1), while blue indicates an opposite correlation (−1–0). The relation withP<0.05 was statistically significant. Statistically insignificant correlation values are signed with a cross.
(a)(b)


## 3.6. Identification of PBRM1-Related Genes and Functional Enrichment Analysis

To further investigate the critical pathways and genes correlated with PBRM1 expression in pan-cancer, we identified the top 200 PBRM1-related genes using GEPIA2. Then, we utilized the STRING instruction to acquire the interactive net of these PBRM1-related genes (Figure6(a)). We also showed the top 5 related genes. The expression of PBRM1 was not negatively associated to that in RAD54L2 (0.8), DENND6A (0.8), DCP1A (0.8), APPL1 (0.82), and SETD2 (0.84) genes (all <0.001) (Figures 6(b)–6(f)).Figure 6
PBRM1-associated genetic expansion and pathway study. (a) Tandem protein network diagram of PBRM1 binding protein determined experimentally. Colored nodes represent individual proteins that have been identified. (b–f) Expressive correlation among PBRM1 and typical genes (RAD54L2, DENND6A, DCP1A, APPL1, and SETD2) of the greatest PBRM1-related genes in TCGA projects determined by GEPIA2. (g, h) GO and KEGG pathway study according to the PBRM1-correlated genes.
(a)(b)(c)(d)(e)(f)(g)(h)In order to analyze the above-related genes at the basic level, we uploaded 300 DEGs online for greater GO and KEGG pathway analysis with DAVID separately. GO analysis about DEGs showed transcriptional regulation of RNA polymerase II promoter, positive transcriptional regulation of RNA polymerase II promoter, positive transcriptional regulation, DNA templatization, chromatin remodeling, transcriptional regulation, DNA templarization, mRNA processing, viral process, mRNA destabilization, protein phosphorylation, regulation of megakaryocyte differentiation, cell division, protein-dependent catabolic process, cell cycle, peptidyl-serine phosphorylation, and ATP-dependent chromatin remodeling (Figure6(g)).Moreover, in KEGG analysis, the genes above were obviously increased in lysine degradation, RNA degradation, protein middle proteolysis, TGF-beta signaling method, and hepatocellular carcinoma (Figure6(h)).


## 4. Discussion

PBRM1 (also known as BAF180) is a subpart in SWI/SNF transcriptionally regulated chromatin remodeling chemical that is an ATP-dependent chromatin-remodeling complex [5]. Previous results showed that PBRM1 could act as a component of the nuclear hormone receptor complex necessary for ligand-dependent transcriptional activation [8]. PBRM1 has been shown to exert tumor suppressor function in breast cancer [5–7]. Moreover, PBRM1 is the second-highest frequently transformative gene of ccRCC and has potential prognostic and clinical significance [16]. PBRM1 is a real tumor suppressor in the pathogenesis of ccRCC, and its expression level is correlated with the prognosis of the disease [10, 11]. All these suggest the potential role of PBRM1 in tumor initiation and development. Nowadays, cancer is currently one of the highest morbidity and mortality rates in the world. Evaluating the roles of critical genes could help us better understand the initiation and development of various tumors. Therefore, to further digging out the role of PBRM1 in other tumor types is an attractive theme to study deeply.In this study, we aimed to estimate the importance of PBRM1 in the expression pattern of generalized carcinoma, immune cell infiltration, and disease prognosis. We also explored related potential mechanisms. We found that PBRM1 was upregulated in CHOL, LIHC, STAD, DLBC, LAML, GBM, LGG, PAAD, OV, and THYM. Moreover, PBRM1 was down of BRCA, BLCA, COAD, KICH, KIRC, KIRP, LUAD, LUSC, PRAD, READ, THCA, and UCEC. Further clinical analysis showed that higher PBRM1 showed better disease outcomes in HNSC, KIRC, and UCEC, while poorer outcomes in KICH, SKCM, and ESCA. These results showed that PBRM1 might exert an oncogenetic or tumor suppressor role in different tissues, indicating the complicated role of PBRM1 in different contexts, which also provides a basis for further research.Currently, the prevailing view is that genetic mutations contribute to the development of cancer, which can increase the cancer cells’ ability in biology to fight adjacent common cells [17–19]. Today, with the development of high-throughput sequencing technologies and systems biology methods, previous studies have offered a number wealth characterizing mutational cancer cells’ heterogeneity [20, 21]. In this research, we utilized the cBioPortal online website to estimate the changing forms of PBRM1 in various cancer types in TCGA. Researchers found that PBRM1 was transformed in the majority of tumors, especially in RCC, and the transformation was the dominant one of genetic substitute in all types. Then, we assessed whether PBRM1 mutation status correlates with the estimation of various cancers. Results of cBioPortal website indicated that RCC cases with mutated PBRM1 showed no different prognosis compared to cases without PBRM1 mutation, which indicates that mutation of PBRM1 might not influence disease development or prognosis of these tumor cases. However, in pan-cancer analysis, patients with mutated PBRM1 showed a better prognosis in overall survival, indicating the potential significance of PBRM1 in different cancer types.In terms of mechanism, we obtained pan-oncogene expression data through GEPIA2 and used bioinformatics analysis to identify genes related to PBRM1 expression. The top 200 related genes were selected for gene functional enrichment analysis, and the above genes were used in GO and KEGG studies. The results showed that the related genes were mainly amplified during DNA or RNA processes. Previous studies have shown that PBRM1 is a subpart of the SWI/SNF transcriptionally regulated chromatin remodeling complex. This is not consistent with our mechanistic analysis. Most of the key genes involved in cancer initiation and progression may influence gene expression through DNA or RNA processes. However, whether PBRM1 can contribute to tumor development through this mechanism needs to be further explored, which may provide concepts for useful therapeutic targets for therapeutic advice.There is one limitation in our study. In our research, the only thing that researchers could do was to investigate the role of PBRM1 in pan-cancer. For the mechanism and role of PBRM1 in other cancers, further clinical and molecular experiments are needed.


## 5. Conclusions

To conclude, the pan-cancer study showed an outline of PBRM1 in multiple cancers of humans, which could promote a comprehensive understanding of PBRM1’s role in tumorigenesis.



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*Source: 7676541-2022-10-12.xml*