Clinical significance and potential regulatory mechanism of overexpression of pituitary tumor-transforming gene transcription factor in bladder cancer

doi:10.21203/rs.3.rs-1530627/v1

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Research Article

Clinical significance and potential regulatory mechanism of overexpression of pituitary tumor-transforming gene transcription factor in bladder cancer

https://doi.org/10.21203/rs.3.rs-1530627/v1

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Background

Pituitary tumor transforming gene-1 (PTTG1) transcription factor is identified carcinogenic and associated with tumor invasiveness, but its roles in bladder cancer (BLCA) remains obscure. This research is intended to analyze the aberrant expression and clinical significance of PTTG1 in BLCA, to explore the relationship between PTTG1 and tumor microenvironment characteristics, and to predict its potential transcriptional activity in BLAC tissue.

Methods

We compared the expression discrepancy of PTTG1 mRNA in BLCA and normal bladder tissue, using the BLCA transcriptomic datasets from GEO, ArrayExpress, TCGA, and GTEx. In-house immunohistochemical staining was implemented to determine the PTTG1 protein intensity. The prognostic value of PTTG1 was evaluated using the Kaplan-Meier Plotter. CRISPR screen data was utilized to estimate the effect PTTG1 interference has on BLCA cell lines. We predicted the immune cells abundance in BLCA tumor microenvironment using the microenvironment cell populations-counter and ESTIMATE algorithms. Single cell sequencing data was applied to identify the major cell types in BLCA cancer, and the dynamics of BLCA progression was revealed using pseudotime analysis. PTTG1 target genes were predicted by CistromeDB.

Results

The elevated expression level of PTTG1 was confirmed in 1037 BLCA samples compared with 127 non-BLCA samples, with a standard mean difference value of 1.04. Higher PTTG1 expression status exhibited poorer BLCA prognosis. Moreover, the PTTG1 Chronos genetic effect scores were negative, indicating PTTG1 silence may inhibit the proliferation and survival of BLCA cells. With PTTG1 mRNA expression level increasing, higher natural killer, cytotoxic lymphocyte, and monocyte lineage cells infiltration levels were observed. A total of four candidate targets containing CHEK2, OCIAD2, UBE2L3 and ZNF367 were determined ultimately.

Conclusions

PTTG1 mRNA over-expression may become a potential biomarker for BLCA prognosis. Additionally, PTTG1 may correlate with BLCA tumor microenvironment and exert transcriptional activity by targeting CHEK2, OCIAD2, UBE2L3, and ZNF367 in BLCA tissue.

BLCA

PTTG1

tumor microenvironment

transcriptional regulation

Bladder cancer (BLCA) is the world's ninth highest cause of cancer-related mortality among men and the second most prevalent malignancy of the human urinary tract in 2020[1]. According to the cancer statistics 2022, there will be approximately 91,893 new cases of BLCA in China, with 42,973 cancer-related mortality[2]. The majority of BLCA patients are non-muscle invasive[3]. Because these two forms of BLCA have fundamentally different biological features, there are major differences in disease onset, overall survival status, and therapeutic regimens[4]. Radical cystectomy is the primary therapeutic strategy for BLCA patients; however, it has a high postoperative recurrence rate, a high incidence of distant metastases, and a worse 5-year survival rate[5]. Therefore, it is urgent to develop novel biomarkers that may be used in early diagnosis, prognostic evaluation, and therapy to improve the survival outcomes of BLCA patients.

The pituitary tumor-transforming gene (PTTG) transcriptional factor (TF) is an oncogenic gene first isolated and discovered in rat cell lines[6]. According to the sequence in which they were identified, there are three PTTG isoforms, namely PTTG1, PTTG2, and PTTG3[7]. PTTG1 is a multifunctional protein implicated and overexpressed in various endocrine-related malignancies, namely pituitary[8], uterine[9], breast[10], and ovarian tumors[11]. Previous research has linked it to the passage of numerous cancer cell types through the metaphase-anaphase transition in cell cycle process[12, 13]. In addition, PTTG1 is substantially connected with tumor invasiveness and is known to be a crucial gene associated with tumor metastasis, whose expression levels in normal human tissue is low[14]. Moreover, PTTG1 could promote the proliferation and metastasis potential of several human tumor types, such as colon cancer[15], esophageal cancer[16], and lung cancer[17], which suggest the pro-tumor role of PTTG1. However, the clinical significance and potential transcriptional regulatory mechanisms of PTTG1 in BLCA are still unclear and require further investigation.

Therefore, the goals of this study were to evaluate the overall expression level of PTTG1 in BLCA tissues and to investigate it is potential clinical value in BLCA patients, as well as the relationship between PTTG1 and tumor immune infiltration, and its transcriptional activity in BLCA tissues.

BLCA tissue samples

Surgery-dissected BLCA and normal bladder tissue specimens were collected from the First Affiliated Hospital of Guangxi Medical University. The inclusion criteria were as follows, (I) the pathological type of BLCA tissue sample should be transitional cell carcinoma; (II) sufficient tissue sample should be prepared for performing tissue microarray and immunohistochemical staining. This study had been approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (2022-KT-GUOJI-146).

In-house immunohistochemistry

To explore the protein expression status of PTTG1 in BLCA, immunohistochemical staining was conducted by using the in-house BLCA and non-BLCA tissues. The rabbit polyclonal antibody to PTTG1 (#orb374037) was purchased from the Biorybt Co., Ltd. (https://www.biorbyt.com/).

The human protein atlas (THPA)

The protein expression of PTTG1 in BLCA tissues and normal bladder tissues were also inquired by using THPA (https://www.proteinatlas.org/). A total of two kinds of antibody were selected, including HPA045034 and CAB008373.

Cancer Dependency Map (Depmap)

The Depmap portal (https://depmap.org/portal/) enables researchers to have a broad view on genetic and pharmacologic dependencies in cancers[18]. Depmap provides large-scale clustered regularly interspaced short palindromic repeats (CRISPR) screen resources, which help in depicting the roadmap of oncology therapeutic targets[19]. Herein, Depmap was used to validate the expression of PTTG1 mRNA in a total of 36 BLCA cell lines. Moreover, the perturbation effects PTTG1 CRISPR has on BLCA cell lines were explored.

Public transcriptome database

Global BLCA gene microarrays and mRNA sequencing data sets were downloaded from the Gene Expression Omnibus (GEO), ArrayExpress, The Cancer Genome Atlas (TCGA), and The Genotype-Tissue Expression project. The inclusion standards were set as follows, (I) the specimen should be human primary BLCA tissue; (II) each platform data set should contain no less than three BLCA samples and three non-BLCA samples. The exclusion standards were as follows, (I) the probe ID annotation information was missing; (II) patient had received preoperative treatment. The enrolled data sets were assigned into different groups according to the affiliated platform. The data sets in each platform were integrated into a larger matrix, named platform matrix. The generated batch effect was removed by using Limma-voom and sva packages.

Differential expression analysis

To portray the global BLCA differentially expressed genes (DEGs) map, the included platform matrices were utilized for calculating standardized mean difference (SMD). Up-regulated gene and down-regulated gene were defined as follows, (I) up-regulated gene: SMD > 0, P < 0.05; (II) down-regulated gene: SMD < 0, P < 0.05.

Prognostic analysis

TCGA-BLCA patients were assigned into PTTG1 high expression group and low expression group according to the optimal cutoff value. Overall survival (OS) and disease-free survival (DFS) Kaplan-Meier survival analysis were conducted to appraise the prognostic value of PTTG1 in BLCA patients by using the Kaplan-Meier Plotter (https://kmplot.com/analysis/index.php?p=service&cancer=pancancer_rnaseq).

Microenvironment cell populations-counter (MCP-counter)

Tumor microenvironment (TME) take an important part in the pathogenesis of malignant tumors[20]. MCP-counter algorithm helps in the cell infiltration quantification of eight immune cells and two stromal cells[21]. The author first downloaded the level three TCGA-BLCA fragments per kilobase of transcript per million fragments mapped (FPKM) data, which were transformed into transcripts per million (TPM) data subsequently. The immune infiltration levels of immune and stromal cells of TCGA-BLCA patients were quantified. Immune, stromal, estimate, and tumor purity scores were calculated by using a method called Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE)[22].

Single cell RNA sequencing (scRNA-seq) dataset analysis

BLCA scRNA-seq dataset GSE135337 were downloaded from GEO [23]. GSE135337 included the single cells mRNA profiles of seven primary BLCA male patients (10X Genomics platform). A total of seven mRNA profiles were aggregated, filtered, and normalized. The filtered criteria were feature RNA > 50 and mitochondria percentage < 5. After principal component (PC) analysis, a total of 16 PC were selected for performing t-distributed Stochastic Neighbor Embedding (tSNE) nonlinear dimensionality reduction analysis. The marker genes of each clustered cell population were identified and were annotated by using SingleR package. Subsequently, epithelial cells were selected for pseudotime analysis by using monocle, and the cell fate related genes were identified and were functionally annotated by gene ontology.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) data analysis

The putative transcriptional targets of PTTG1 were downloaded from CistromeDB (http://cistrome.org/db/#/) (ID: 63264 and 63265)[24, 25]. The transcriptional targets of PTTG1 were acquired by intersecting cell fate related genes, BLCA DEGs, and putative TF targets. The interaction network between PTTG1 TF and the intersected targets were analyzed by using STRING v11.5 (https://string-db.org/)[26]. The most closely interacted genes were predicted to be PTTG1 transcriptional targets.

Statistical analysis

All the statistical analyses were completed by using R v4.0.4. Wilcoxon or Kruskal-Wallis test were selected to compare the difference of PTTG1 expression levels between two or more than three groups. According to the result of heterogeneity test, a fixed or randomized effect model was selected for pooling SMD values (a fixed effect model for I² > 50%, while a randomized effect model for I² > 50%). The discriminatory ability of PTTG1 was appraised by integrating true positive, false positive, false negative, and true negative rates into a summary receiver operating characteristic curve. The area under the curve (AUC) was used to indicate the discriminatory ability of PTTG1, where AUC < 0.7, 0.7 ≤ AUC < 0.9, 0.9 ≤ AUC represented a weak, moderate, and strong ability.

PTTG1 over-expression in BLCA tissues and cells

A total of 47 BLCA and 10 non-BLCA tissue specimens were utilized to perform in-house immunohistochemical staining. PTTG1 antibody was strongly stained in BLCA tissue as was opposed to non-BLCA control tissue (Fig. 1). Consistently, according to the immunohistochemical result from THPA, PTTG1 antibody was moderately stained in urothelial carcinoma of bladder and were lowly stained or not detected in normal or inflammatory bladder tissues (Antibody: HPA045034 and CAB008373) (Fig. 2), which confirmed the elevated protein expression level of PTTG1 in BLCA.

Furthermore, the increased expression level of PTTG1 mRNA were supported by gene microarrays and mRNA sequencing data sets (Fig. 3A). The overall expression trend of PTTG1 in BLCA was assessed in global BLCA tissues. In general, the expression level of PTTG1 was significantly increased in 1037 BLCA tissue samples compared with 127 non-BLCA control tissue samples, with a SMD of 1.04 [0.17, 1.91] (Fig. 3B). No bias was observed (Fig. 3C). However, the forest plot result of sensitivity analysis implied that the SMD result may be unstable (Fig. 3D). Therefore, more experimental verification is needed in the future.

As is shown in Fig. 4A, the relative expression levels of PTTG1 mRNA were also compared in pan-cancer cell lines. PTTG1 mRNA was broadly expressed in a series of bladder carcinoma cell lines (Fig. 4B). More importantly, the Chronos gene effect scores were all less than zero from CRISPR loss of function screens in BLCA cell lines (Fig. 4C), indicating that PTTG1 deletion may suppress the proliferation and survival of BLCA cells.

In-house immunohistochemistry was conducted to explore the protein expression level of PTTG1, and PTTG1 protein was overexpressed in bladder carcinoma (BLCA) compared with non-BLCA.

The protein expression level of PTTG1 was confirmed by the immunohistochemistry result from the human protein atlas. PTTG1 protein was moderately stained in urothelial carcinoma of bladder and were lowly stained or not detected in normal or inflammatory bladder tissues (Antibody: HPA045034 and CAB008373).

Potential clinical value of PTTG1 in BLCA patients

In subsequent analysis, the authors explored the potential clinical implication of PTTG1 in BLCA patients. PTTG1 showed a strong discriminatory ability between BLCA tissues and normal bladder tissues (Fig. 5A–D, Fig. 6A), with an AUC value of 0.93, a high sensitivity (0.89 [0.81, 0.95]), and a high specificity (0.82 [0.70, 0.93]). The accuracy of PTTG1 in differentiating BLCA from normal tissues were confirmed by a high positive likelihood ratio (6.11 [2.73, 15.71]) and a low negative likelihood ratio (0.13 [0.05, 0.26]). Additionally, higher PTTG1 mRNA levels presaged worse OS probability in TCGA-BLCA cohort (sample size: 404) (Fig. 6B). Although insignificant, higher PTTG1 mRNA levels tended to show poorer DFS outcomes (sample size: 187) (Fig. 6C). The result of time dependent receiver operating characteristic curve showed the weak prognostic ability of PTTG1 in TCGA-BLCA cohort (Fig. 6D).

PTTG1 showed a strong discriminatory ability between bladder carcinoma tissues and normal bladder tissues, with a high sensitivity (A) and a high specificity (B). The accuracy of PTTG1 in differentiating bladder carcinoma from normal tissues were confirmed by a high positive likelihood ratio (C) and a low negative likelihood ratio (D).

Association between PTTG1 expression and BLCA TME

As is shown in Fig. 7A, the author first compared the immune microenvironment and clinical phenotypes among normal bladder tissues, PTTG1 low expression BLCA tissues, and PTTG1 high expression BLCA tissues. It was observed that higher PTTG1 mRNA expression levels indicated higher natural killer (NK), cytotoxic lymphocyte, and monocyte lineage cell infiltration levels, and it indicated lower neutrophils and endothelial cell infiltration levels (Fig. 7B). Additionally, PTTG1 expression level positively correlated to the infiltration degrees of dendritic cell (R = 0.416, P = 1.12e − 16). The immune score was significantly lower in low PTTG1 mRNA expression BLCA tissues than that in high PTTG1 mRNA expression BLCA tissues or normal bladder tissues (Fig. 7C). However, no difference was detected in the stromal score of low PTTG1 mRNA expression BLCA tissues than that in high PTTG1 mRNA expression BLCA tissues (Fig. 7C). Moreover, a high stromal score predicted poor prognosis in BLCA patients (Fig. 7D). The prognostic value of immune score in BLCA patients was insignificant (Fig. 7E).

Prospective transcriptional mechanisms of PTTG1 in BLCA at the single cell resolution

Given the intimate association between PTTG1 and BLCA, the author subsequently explored its potential transcriptional mechanisms underlying BLCA. Single cells from seven BLCA patients were clustered and annotated by using GSE135337 scRNA-seq profile, where cancerous epithelial cells were the predominant cell types (Fig. 8A). We tried to identify the cell distribution of PTTG1 expression, and it was found that PTTG1 were mainly expressed in the epithelial cells (Fig. 8B). Next, epithelial cells from BLCA tissues were subsetted for performing pseudotime analysis, through which we could infer the cell fate of cancerous cells and remodel the process of cell changes over time (Fig. 8C). Figure 8D showed the gene expression heatmap in the epithelial cells along the pseudotime direction. The cell fate related DEGs were functionally clustered in the negative regulation of cell cycle process, nuclear-transcribed mRNA catabolic process, nonsense-mediated decay, response to topologically incorrect protein, and RNA splicing.

Finally, a total of 73 genes were preserved by intersecting cell fate related DEGs, BLCA DEGs, and putative TF targets. By studying the protein-protein interaction network, CHEK2 (checkpoint kinase 2), OCIAD2 (OCIA domain containing 2), UBE2L3 (ubiquitin conjugating enzyme E2 L3), and ZNF367 (zinc finger protein 367) were predicted to be the transcriptional targets of PTTG1 in BLCA (Fig. 9A). More importantly, ChIP-seq data were used to explore the binding peak of PTTG1 in the promoter regions of such four targets (Fig. 9B), indicating the transcriptional regulatory relationship between them. We also preliminarily validated the transcriptional targets of PTTG1 in BLCA by differential and co-expression analysis. As is shown in Figure S1, CHEK2, OCIAD2, UBE2L3, and ZNF367 all displayed increased expression trends in the TCGA-BLCA cohort. Moreover, the over-expression of such targets were certified by the global BLCA mRNA datasets (CHEK2: SMD = 0.75; OCIAD2: SMD = 1.38; UBE2L3: SMD = 0.58; ZNF367: SMD = 0.59) (Fig. 10). Furthermore, there were significant positive correlation between PTTG1 and such four targets (CHEK2: Spearman R = 0.44; OCIAD2: Spearman R = 0.32; UBE2L3: Spearman R = 0.37; and ZNF367: Spearman R = 0.61), thus suggesting that PTTG1 may positively regulating the transcriptional activity of CHEK2, OCIAD2, UBE2L3, and ZNF367 targets.

Herein, the authors investigated the global expression status and prospective transcriptional regulation mechanisms of PTTG1 in BLCA. PTTG1 was significantly over-expressed in 1037 BLCA tissue samples and showed a strong ability in distinguishing BLCA tissue from normal bladder tissue. High PTTG1 expression showed increased antitumor activity in BLCA tissues, with elevated infiltration levels of NK, cytotoxic lymphocytes, and monocytic lineage cells. More importantly, the authors preliminarily identified the positive transcriptional activity between PTTG1 and CHEK2, OCIAD2, UBE2L3, and ZNF367.

The over-expression trend of PTTG1 in BLCA were certified using multi-faceted data sets. To probe the comprehensive expression status of PTTG1 in BLCA tissues, we made full use of the in-house immunohistochemistry data and external expression matrices, and a total of 1037 BLCA tissue specimens as well as 127 normal bladder tissue specimens were integrated. Consistently, we observed the increased expression trends of PTTG1 at both tissue and cell levels. Moreover, PTTG1 mRNA may be a strong distinguishing biomarker for BLCA and its over-expression could presage poor OS condition in BLCA patients. In summary, it is suggested that PTTG1 mRNA over-expression may operate as a cancer-promoting factor and could have the potential to be a predictor of poor prognosis in BLCA.

Furthermore, the potential PTTG1 activity in BLCA TME was investigated. As is well known, the abnormal TME is a hotbed for cancer initiation and progression[27], where immune and stromal cells take an important part[28]. Among them, mononuclear phagocyte system constitute an essential part of human tumor immunity; and cytotoxic lymphocytes, together with NK cells, constitute an important defense line in anti-tumor immunity [29]. Previous study has reported that higher PTTG1 expression could be found in the CD⁴⁺ and CD⁸⁺ T lymphocytes of adult T-cell leukemia patients than that of healthy subjects[30], which implied an intimate association between PTTG1 and lymphocytes. According to the Rostyslav Stoika et al., the mRNA abundance of PTTG1 corresponded to the increase in S-phase cells during the activation of T lymphocytes[31], which suggested that PTTG1 expression may follow the cell cycling patterns in T cells. In the present study, a high PTTG1 mRNA expression group was shown to be enriched with NK, cytotoxic lymphocyte, and monocyte lineage cells in BLCA patients. Furthermore, the immune score was significantly higher in high PTTG1 mRNA expression group than that in low PTTG1 mRNA expression group in BLCA tissues. Moreover, there was a positive association between the mRNA expression levels of PTTG1 and dendritic cell infiltration levels, which reflexed the antigen-presenting activity in BLCA tissue. In this setting, we inferred that PTTG1 may serve as an oncogene and possess immunogenicity in BLCA, which has the potential to be designed as mRNA vaccine in the future. Intriguingly, it has been shown that SP17/AKAP4/PTTG1 could induce an immunogenic response in non-small cell lung cancer patients[32]. Moreover, higher expression level of PTTG1 correlated to immune checkpoint response in papillary renal cell carcinoma cohort[33]. Such evidences pointed out that PTTG1 may be a promising immunotherapeutic target for BLCA patients. More experiments are required to be performed to promote the researches of novel cancer vaccine for BLCA in the future study.

The prospective transcriptional mechanisms of PTTG1 were preliminarily portrayed in BLCA. CHEK2, OCIAD2, UBE2L3, and ZNF367 were predicted to be four positive transcriptional targets of PTTG1 in BLCA. Multiple studies have reported these four transcriptional targets of PTTG1 in tumor tissue. For instance, researchers performed immunohistochemical staining in BLCA samples and showed that CHEK2 protein expression surpassed 11 percent in 115 out of 126 BLCA tissue specimens[34]. Additionally, CHEK2 mutation was reported to be a risk factor for the recurrence of BLCA[35]. Similarly, OCIAD2, UBE2L3, and ZNF367 also displayed intimate association with cancer development. In previous function assays, OCIAD2 was showed to be essential for the activation of signal transducer and activator of transcription 3 and cell migration, which suggested that OCIAD2 may contribute to the metastasis of cancer cells[36]. In liver cancer and oral squamous cell carcinoma, UBE2L3 was reported to be an important pro-tumorigenic factor in carcinogenesis [37, 38] and may be a potential treatment target for hepatocellular carcinoma[39]. Moreover, ZNF367 could induce the transcriptional activation of kinesin family member 15, leading to elevated cell viability and invasion ability in breast cancer cells[40]. Nonetheless, the biological functions of OCIAD2, UBE2L3, and ZNF367 in BLCA remained obscure, and more experimental exploration are required to understand their roles in BLCA development. Generally, the predicted transcriptional mechanism results supported the previous speculation that over-expression of PTTG1 seemed to be associated with the initiation and development of BLCA.

The present study has numerous highlights. Using integrated in-house immunohistochemical data from our institution, TCGA, and GEO datasets, we fully discovered PTTG1 over-expression in BLCA and its potential as a biomarker and prognostic value. The limitations of this study could not be overlooked. First, despite the fact that we combined datasets from multiple sources, the high-degree heterogeneity could not be eliminated by the randomized effect model; second, the total sample size was limited, and more experimental studies must be carried out in the future to demonstrate our findings and clarify the specific mechanism of PTTG1 in the development and progression of BLCA.

PTTG1: Pituitary tumor transforming gene-1; BLCA: bladder cancer; GEO: Gene Expression Omnibus; TCGA: The Cancer Genome Atlas; GTEx: Geotype-Tissue Expression; TF: Transcriptional factor; THPA: The Human Protein Atlas; Depmap: Cancer Dependency Map; CRISPR: Clustered Regularly Interspaced Short Palindromic Repeats; DEGs: Differently expressed genes; SMD: Standardized mean difference; OS: Overall survival; DFS: Disease-free survival; TME: Tumor microenvironment; FPKM: Fragments per kilobase of transcript per million fragments mapped; TPM: Transcript per million; scRNA-seq: Single cell RNA sequencing; PC: Principal component; tSNE: t-distributed Stochastic Neighbor Embedding; ChIP-seq: Chromatin immunoprecipitation followed by sequencing; AUC: Area under the curve; NK: Natural killer; CHEK2: Checkpoint kinase 2; OCLAD2: OCTA domain containing 2; UBE2L3: Ubiquitin conjugating enzyme E2 L3; ZNF367:Zinc finger protein 367.

Acknowledgements

We are grateful for the technical support provided by Guangxi Key Laboratory of Medical Pathology.

Authors' contributions

JDL and SHL designed the study. GQZ, RGW and JLL participated in data collection. ZGH performed the immunohistochemical experiment. JDL and AAF carried out data analysis. YWD provided guidance. JDL, ZLL, QJW and GLZ drafted the manuscript. All authors read and approved the final manuscript.

Funding

Fund of Natural Science Foundation of Guangxi, China (2018GXNSFAA281175), Guangxi Clinical Research Center for Urology and Nephrology (No.2020AC03006), Guangxi Science and Technology Base and Talent Project (No.2019AC17009), Medical Excellence Award Funded by the Creative Research Development Grant from the First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University "Future Academic Star" project (WLXSZX21036), Guangxi Medical University 2020 Innovation and Entrepreneurship Training Program for College Students (202110598060), and 2021 First Clinical Medical College Science and Technology Innovation Training Program.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in Gene Expression Omnibus [https://www.ncbi.nlm.nih.gov/geo/], ArrayExpress [https://www.ebi.ac.uk/arrayexpress/], the Cancer Genome Atlas [https://portal.gdc.cancer.gov/], Cancer Dependency Map [https://depmap.org/portal/]. Kaplan-Meier Plotter is publicly available at http://kmplot.com/analysis/.

Ethics approval and consent to participate

This study was approved by the ethics committee of the First Affiliated Hospital of Guangxi Medical University (2022-KT-GUOJI-146). The authors confirmed that all methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects.

Consent for publication

Not applicable.

Competing interests

All authors declare no conflict of interest in this study.

Author details

¹ Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Rd, Nanning, Guangxi Zhuang Autonomous Region, 530021, P.R. China;² Department of Urology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Rd, Nanning, Guangxi Zhuang Autonomous Region, 530021, P.R. China.

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No competing interests reported.

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Clinical significance and potential regulatory mechanism of overexpression of pituitary tumor-transforming gene transcription factor in bladder cancer

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Version 1

Abstract

Figures

Background

Methods

BLCA tissue samples

In-house immunohistochemistry

The human protein atlas (THPA)

Cancer Dependency Map (Depmap)

Public transcriptome database

Differential expression analysis

Prognostic analysis

Microenvironment cell populations-counter (MCP-counter)

Single cell RNA sequencing (scRNA-seq) dataset analysis

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) data analysis

Statistical analysis

Results

PTTG1 over-expression in BLCA tissues and cells

Potential clinical value of PTTG1 in BLCA patients

Association between PTTG1 expression and BLCA TME

Prospective transcriptional mechanisms of PTTG1 in BLCA at the single cell resolution

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1