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Comprehensive Analysis of Tumour Mutational Burden and Its Clinical Signifcance in Prostate Cancer Lijuan Wang, Shucheng Pan, Binbin Zhu, Zhenliang Yu and Wei Wang*

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Comprehensive Analysis of Tumour Mutational Burden and Its Clinical Signifcance in Prostate Cancer Lijuan Wang, Shucheng Pan, Binbin Zhu, Zhenliang Yu and Wei Wang* Wang et al. BMC Urol (2021) 21:29 https://doi.org/10.1186/s12894-021-00795-7 RESEARCH ARTICLE Open Access Comprehensive analysis of tumour mutational burden and its clinical signifcance in prostate cancer Lijuan Wang, Shucheng Pan, Binbin Zhu, Zhenliang Yu and Wei Wang* Abstract Background: The tumorigenesis of prostate cancer involves genetic mutations. Tumour mutational burden (TMB) is an emerging biomarker for predicting the efcacy of immunotherapy. Results: Single-nucleotide polymorphisms were the most common variant type, and C>T transversion was the most commonly presented type of single-nucleotide variant. The high-TMB group had lower overall survival (OS) than the low-TMB group. TMB was associated with age, T stage and N stage. Functional enrichment analysis of diferentially expressed genes (DEGs) showed that they are involved in pathways related to the terms spindle, chromosomal region, nuclear division, chromosome segregation, cell cycle, oocyte meiosis and other terms associated with DNA mutation and cell proliferation. Six hub genes, PLK1, KIF2C, MELK, EXO1, CEP55 and CDK1, were identifed. All the genes were associated with disease-free survival, and CEP55 and CDK1 were associated with OS. Conclusions: The present study provides a comprehensive analysis of the signifcance of TMB and DEGs and infltrat- ing immune cells related to TMB, which provides helpful information for exploring the signifcance of TMB in prostate cancer. Keywords: Prostate cancer, Tumour mutational burden, Gene, Clinical Background utilization of immunotherapy is still limited by low ef- Prostate cancer ranks as the second most commonly cacy. Some response predictive biomarkers are under diagnosed malignancy in males [1]. In terms of treat- investigation, including tumour mutational burden ment, challenges still exist, especially for castration- (TMB). resistant prostate cancer (CRPC). CRPC with metastasis TMB involves the number of non-synonymous somatic is reported to have a poor prognosis, with a median sur- mutations per megabase pair (Mbp) of sequenced DNA. vival time of less than 2 years [2]. In recent years, immune Mutations of tumours afect the mutational load, which checkpoint inhibitors blocking programmed cell death 1 in turn determines the chance of presenting immunogen- and its ligand (PD-1/PD-L1) and cytotoxic T-lymphocyte ically relevant neoantigens [5]. High-TMB tumours tend antigen-4 (CTLA-4) have shown promising preliminary to harbour more neoantigens than low-TMB tumours, results in various kinds of tumours. Two clinical trials of which make the high-TMB tumours more immunogenic, ipilimumab in metastatic CRPC have shown improved resulting in an improved T cell response and subsequent progression-free survival [3, 4]. Nevertheless, the enhancement of antitumour immunity. Given the func- tion of TMB in immunity, clinical studies focused on *Correspondence: [email protected] melanoma and non-small-cell lung cancer have demon- The First Afliated Hospital, Zhejiang University School of Medicine, strated that the TMB is associated with the immunother- Hangzhou 310003, Zhejiang Province, China apy treatment response [6, 7]. Terefore, TMB is believed © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Wang et al. BMC Urol (2021) 21:29 Page 2 of 10 to be one of the candidates for predicting the efcacy of labels. Te pathways were considered to be statistically immunotherapy. enriched according to a cutof of FDR < 0.25. Next-generation sequencing (NGS) profling of patients has enabled advancements, and Te Cancer Genome Protein–protein interaction (PPI) network and classifcation Atlas (TCGA) ofers convenient access to relevant infor- of core genes mation. In this study, the gene expression and muta- tion profles of prostate cancer samples were extracted We constructed a PPI network of DEGs using STRIING from TCGA, and the data were used to investigate the (https ://strin g-db.org/). Evidence-based interactions clinical signifcance of TMB and its related diferentially were generated with a minimum required interaction expressed genes (DEGs) and immune cell infltration score > 0.4 [9]. Ten, the PPI network was visualized by signatures. utilizing Cytoscape 3.8.0 software [10]. Using the Cyto- Hubba plugin, we obtained fve protein groups, includ- Methods ing the top 30 proteins, with fve analytic algorithms, Data collection and processing including MCC, MNC, Degree, EPC and EcCentricity, Transcriptome data in the HTSeq‐FPKM format from as previously reported [11]. In addition, we acquired 524 samples, including 499 prostate and 25 adjacent the overlapping proteins of the fve groups and identi- normal tissue samples, were acquired from the TCGA fed pivotal proteins with more interactions than others databank (https ://porta l.gdc.cance r.gov/). Te data were as potential hub proteins. Tese intersections were visu- in masked somatic mutation fles, and these data were alized by a Venn diagram, which was generated online analysed and visualized using the R software package (http://bioin forma tics.psb.ugent .be/webto ols/Venn/). maftools. GEPIA (http://gepia .cance r-pku.cn/) was used to investi- gate the prognostic signifcance of the hub proteins. Estimation of TMB and its associations with clinical factors TMB was defned as the sum of mutations in coding CIBERSORT regions per megabase and was calculated as the total CIBERSORT is an algorithm for characterizing the number of mutations/the length of exons. Te length of immune cell composition of certain tissues according to exons was estimated to be 38 Mb in previous research their gene expression profles [12]. We used CIBERSORT [8]. First, Perl scripts were written to obtain the esti- in the R package to analyse 22 types of immune cells. Te mated TMB data, which were amalgamated with inter- comparison of the levels of immune cells between the related survival profles for each patient. Ten, we set group with low TMB and the group with high TMB was the median TMB value as the cutof, according to which conducted using the Wilcoxon rank‐sum test, and the samples were classifed into a group with high TMB and a results were presented in a violin diagram generated with group with low TMB. We utilized the survival R package the vioplot package of the R package. to analyse the overall survival (OS) diferences between the two groups. Te diferences in TMB between groups categorized based on clinical features (age, T stage and N Statistical analysis stage) were analysed using the limma R package. We used the Kaplan–Meier method to generate the sur- vival curve. Te comparisons of OS between diferent Identifcation of DEGs and DEG pathway analysis groups categorized according to TMB, age, T stage and First, we performed DEG analysis with |log2 fold N stage was performed using the log‐rank test. We used change > 1| and false discovery rate (FDR) < 0.05 as cut- the Shapiro–Wilk test to determine whether the groups ofs. We employed the limma package in the R package categorized according to TMB, age, T stage and N stage to analyse the diferences and used pheatmap in the R had normal distributions. Trough the Shapiro–Wilk package to generate a heatmap. Subsequently, the gene test, we found that all the groups were not normally dis- symbols were transferred into the ID of Entrez using the tributed, and thus, nonparametric tests were required. org.Hs.eg.db package of the R package. Moreover, we Te Wilcoxon rank‐sum test was used to compare groups employed the ggplot2, enrichplot, and clusterProfler of classifed by age and N stage. Te Kruskal–Wallis test R packages to perform Gene Ontology (GO) and Kyoto was used to compare groups classifed by T stage. We Encyclopedia of Genes and Genomes (KEGG) analyses of used R software (version 3.6.1) to perform the Kaplan– the DEGs. We used GSEA software (https ://www.gsea- Meier analysis, log‐rank test, Wilcoxon rank‐sum test and msigd b.org/gsea/index .jsp) to perform GSEA. We used Kruskal–Wallis test. We used SPSS (version 25.0) to per- c2.cp.kegg.v7.0.symbols.gmt as the databank of gene form the Shapiro–Wilk test. Te criterion for statistical sets. In addition, the TMB level was set as the phenotype signifcance was a P value < 0.05. Wang et al. BMC Urol (2021) 21:29 Page 3 of 10 Results PPI network of DEGs and selected hub genes Mutations in prostate cancer A PPI network of 189 nodes and 1789 edges was gener- We obtained mutation data from TCGA, which were ated by STRING, and online tool for analysing proteins, analysed and visualized with the maftools package. Mis- and the results were visualized by Cytoscape (Fig. 4a). sense mutations were the most common type of variant Five algorithms, MCC, MNC, Degree, EPC and EcCen- (Fig. 1a). Te frequency of single-nucleotide polymor- tricity, were utilized, and the overlapping proteins in phisms (SNPs) was greater than that of other variant the results generated from each algorithm were iden- types (Fig.
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