Dai and Liu J Transl Med (2021) 19:206 https://doi.org/10.1186/s12967-021-02870-x Journal of Translational Medicine RESEARCH Open Access High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells Zao Dai and Ping Liu* Abstract Background: Tumor metastasis is the main cause of death of cancer patients, and cancer stem cells (CSCs) is the basis of tumor metastasis. However, systematic analysis of the stemness of prostate cancer cells is still not abundant. In this study, we explore the efective factors related to the stemness of prostate cancer cells by comprehensively min- ing the multi-omics data from TCGA database. Methods: Based on the prostate cancer transcriptome data in TCGA, gene expression modules that strongly relate to the stemness of prostate cancer cells are obtained with WGCNA and stemness scores. Copy number variation of stemness genes of prostate cancer is calculated and the diference of transcription factors between prostate cancer and normal tissues is evaluated by using CNV (copy number variation) data and ATAC-seq data. The protein interac- tion network of stemness genes in prostate cancer is constructed using the STRING database. Meanwhile, the correla- tion between stemness genes of prostate cancer and immune cells is analyzed. Results: Prostate cancer with higher Gleason grade possesses higher cell stemness. The gene set highly related to prostate cancer stemness has higher CNV in prostate cancer samples than that in normal samples. Although the tran- scription factors of stemness genes have similar expressions, they have diferent contributions between normal and prostate cancer tissues; and particular transcription factors enhance the stemness of prostate cancer, such as PUM1, CLOCK, SP1, TCF12, and so on. In addition, the lower tumor immune microenvironment is conducive to the stemness of prostate cancer. CD8 T cells and M1 macrophages may play more important role in the stemness of prostate can- cer than other immune +cells in the tumor microenvironment. Finally, EZH2 is found to play a central role in stemness genes and is negatively correlated with resting mast cells and positively correlated with activated memory CD4 T cells. + Conclusions: Based on the systematic and combined analysis of multi-omics data, we fnd that high copy number variation, specifc transcription factors, and low immune microenvironment jointly contribute to the stemness of prostate cancer cells. These fndings may provide us new clues and directions for the future research on stemness of prostate cancer. Keywords: Stemness of prostate cancer, WGCNA, ATAC-seq, CNV, Immune infltration *Correspondence: [email protected]; [email protected] College of Life Sciences, Nanjing Normal University, Nanjing, Jiangsu, China © The Author(s) 2021. 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. Dai and Liu J Transl Med (2021) 19:206 Page 2 of 13 Background analysis showed that stemness-related genes had higher Cancer stem cells (CSCs) are a few stemness-like cells CNV in prostate cancer than that in prostate nor- with the ability of self-renewal and diferentiation in mal samples. Transcription factors of stemness genes cancers [1]. Tey play an important role in the occur- enhanced the stemness of prostate cancer cells. Te rence and development of cancers, especially closely stemness of prostate cancer cells was negatively corre- related to cancer metastasis [2–4]. In prostate cancer, lated with the immune response; and low immune scores the stemness of cancer cells (including prostate cancer were benefcial to the prostate cancer stemness. EZH2 stem cells, PCSC) is closely related to the metastasis of was found to play a central role in these stemness genes. prostate cancer [5]. In the metastasis process of prostate All our multi-omics analyzing results might provide cancer, PCSCs initiates EMT (epithelial-mesenchymal some theoretical clues for us to experimentally investi- transition) to form fbroblast-like cells and then enter the gate the factors afecting the PCa cell stemness and its blood. With the circulation system, prostate cancer cells relationships between PCSC and PCa metastasis. migrate to other tissues (such as bone tissue and lymph tissue) and grow into tumor tissues, which leads to tumor Methods metastasis (cancer cell spreading). Analysis of clinical data As we known, there are many factors which relate to We obtained the clinical information of prostate cancer the stemness of CSC cells, including both intracellular from TCGA database and then divided prostate cancer factors (such as stemness-related genes) and microenvi- samples into fve grades according to the Gleason score ronment of cancer tissues (such as immune cells in the and named Gleason grade 1 (Gleason score 6), Gleason tumor microenvironment) [3, 6, 7]. In prostate cancer grade 2 (Gleason score (3 + 4)), Gleason grade 3 (Gleason (PCa), it has been reported that the immune cells (espe- score (4 + 3)), Gleason grade 4 (Gleason score 8), Gleason cially CD8 + T cells and macrophages) in the microen- grade 5 (Gleason score 9 or 10), respectively. vironment of PCa are closely related to the metastasis of PCa cells [8, 9]. Te number of immune cells around Analysis of transcriptome (RNA‑seq) data the early PCa tissue will be decreased with the growth Based on OCLR stemness scores and Gleason classif- of the cancer tissue, which results in the decrease of the cation of TCGA prostate cancer clinical data, correla- immunity in the microenvironment of PCa [10]. With the tion analysis between PCa cell stemness and Gleason development of PCa to the later stage (Gleason score to grade was carried out. By combing the stemness score, 6–10), some immune cells (such as related T cells) in the WGCNA [19] analysis was performed on the transcrip- microenvironment can reverse to promote or enhance tome data of prostate cancer in TCGA. Diferential the growth of PCa and help cancer cell metastasis [11]. expression analysis of genes in the WGCNA results that Although increasing evidences have shown the rela- mostly related to PCa cell stemness was performed and tionship between stemness and metastasis in PCa cells, presented in a heatmap. Further, the normalized expres- few studies on the stemness regulation of PCa cells are sion of stemness genes was obtained by using between- reported [12, 13]. Lots of factors afecting the stemness array normalization of the limma [20] package to analyze of PCa cells remain unclear and need to be investigated. the transcriptome data of 33 samples (GSE104786) from Te bioinformatics method based on the TCGA data- GEO database and then presented in a heatmap. Also, the base has been increasingly used to analyze the molecular transcriptome data from SRA database (Normal samples basis of prostate cancer development and clinical patient SRR7651698, SRR7651699, SRR7651700), PCa cell lines prognosis [14–16]. Using appropriate analysis software (SRR7651715, SRR7651716, SRR7651717, SRR7651718), and methods to explore a variety of large-size data of and other small cell prostate cancer (SRR7651719, clinical specimen from the TCGA database (including SRR7651720) were aligned and quantifed by using transcriptome sequencing data, gene sequencing data, HISAT2 [20] and HTSeq [21], respectively. Finally, the ATAC-seq data, etc.), the molecular basis of prostate normalized expression data was obtained by using TPM tumorigenesis, development of PCa, and the prognosis of (Transcript per million) normalization method. patients may be fgured out [17, 18]. Terefore, the bio- informatics analysis of TCGA data can provide clues and Analysis of gene CNV data directions for both the basic experimental research and Te genes most related to stemness in WGCNA results the clinical cancer treatment in PCa. were screened, and their locations in genome were In this study, we frstly obtained the most important obtained by local Perl script method. Combined with stemness-related modules and genes in prostate can- the CNV data of TCGA, the local Perl script was used to cer cells from transcriptome data and OCLR scores (a screen the segments containing the locations of impor- method for scoring the stemness of tumors) [2]. Further tant stemness genes. Te prostate cancer samples were Dai and Liu J Transl Med (2021) 19:206 Page 3 of 13 classifed by Gleason grade and then the CNVs of essen- Results tial stemness genes in each type of prostate cancer sam- Stemness‑related key genes are highly expressed in high ple were calculated by using GISTIC2.0 [22]. Gleason grade prostate cancer samples Based on the joint analysis of the OCLR’s results and Analysis of ATAC‑seq data transcriptome data of PCa clinical specimen in TCGA, From TCGA and SRA database, we got the ATAC-seq it’s found that the stemness of PCa cells of Gleason data, including normal prostate samples (SRR7651660, grade 3–5 was signifcantly higher than that of Glea- SRR7651661, SRR7651662), prostate cancer samples son grade 1–2 (Fig.