The PROTTER database (https://wlab.ethz.ch/protter/start/) was utilized to forecast protein sequence characteristics and exhibit the transmembrane topology of proteins using the experimental proteomics evidence set of PTMs [24]. The Human Protein Atlas (HPA) database, available at https://www.proteinatlas.org/, utilizes a variety of techniques, including antibody-based imaging, mass spectrometry-based proteomics, transcriptomics, and systems biology to comprehensively map all human cells, tissues, and proteins in the organs [25, 26]. We obtained the pan-cancer dataset from the UCSC database, which was unified and standardized. This dataset, named TCGA TARGET GTEx (PANCAN, N = 19,131, G = 60,499), was used to extract the gene expression data of ENSG00000114346 (PTTG1) in each sample.

We obtained the harmonized pan-cancer dataset from the UCSC database (https://xenabrowser.net/), specifically the TCGA TARGET GTEx dataset (PANCAN, N = 19,131, G = 60,499). Subsequently, we extracted the gene expression data for ENSG00000164611 (PTTG1) in each sample from TCGA Target GTEx (PANCAN, N = 19,131, G = 60,499). Then, the sample sources were filtered based on the following criteria: Primary blood-derived cancer–peripheral blood (TCGA-LAML), primary tumor, TCGA-SKCM metastatic, primary blood-derived cancer–bone marrow, primary solid tumor, recurrent blood-derived cancer–bone marrow samples. Additionally, we obtained high-quality prognostic TCGA datasets from research previously published in Cell using the TCGA prognosis. After excluding samples with a follow-up time of less than 30 days, we transformed each expression value using log2(x+0.001). Finally, cancer samples with fewer than 10 samples in a single cancer were excluded, resulting in a final dataset comprising 44 cancer samples.

To study the genetic changes in the pan-cancer cohort regarding the PTTG1 gene, we logged into the cBioPortal website (https://www.cbioportal.org/) [27, 28]. To query the genetic alteration signature of PTTG1, we selected the ‘TCGA Pan-Cancer Atlas Studies’ in the ‘Quick Selection’ section. The Cancer Types Summary module was used to view the mutation frequency, mutation type, and copy number alteration (CNA) results for all TCGA tumors. Additionally, the mutation module displayed the PTTG1 mutation site information in the protein structure diagram or three-dimensional structure.

We obtained the pan-cancer dataset from the UCSC database (https://xenabrowser.net/). The dataset, which included TCGA TARGET GTEx (PANCAN, N = 19,131, G = 60,499), was used to extract the ENSG00000164611 (PTTG1) genes and 150 marker genes for the five types of immune pathways (chemokine (41), receptor (18), MHC (21), Immunoinhibitor (24), Immunostimulator (46)) from each sample expression data. To calculate the stromal, immune, and ESTIMATE scores of each patient in each tumor based on gene expression, we utilized the R package ESTIMATE. The collated gene expression data was combined with the measured infiltration scores of various cell types for every patient in each tumor using the “Timer” [29], “deconvo_epic”, and “deconvo_quantiseq” algorithms in the R package “IOBR”. The results were visualized using the R package “reshape2” and “RColorBreyer”. The Tumor Immunity Single Cell Center (TISCH) has compiled a comprehensive collection of 76 tumor datasets from 27 different cancer types. This includes single-cell transcriptome profiles of nearly 2 million cells, providing an invaluable resource for researchers studying tumor immunity [30].

We obtained the pan-cancer dataset from the UCSC (https://xenabrowser.net/) database, which was unified and standardized. This dataset, known as TCGA pan-cancer (PANCAN), consisted of 10,535 samples and 60,499 genes. We specifically extracted the gene expression data of ENSG00000164611 (PTTG1) from each sample. To ensure the highest quality data, we further screened the samples only to include those from primary blood-derived cancers in peripheral blood and primary tumor samples [31], and we calculated the TMB (tumor mutation burden), MSI (microsatellite instability), purity, for the DNA of each tumor using the R package maftools [32, 33, 34]. The gene expression data from the samples underwent integration before being transformed using the log2(x+0.001) formula. Cancer types with less than three samples were eliminated to ensure data accuracy, resulting in a final dataset of expression data for 37 cancer types.

The SK-N-SH cell line was obtained from the Chinese Academy of Sciences and cultured in DMEM (Yeasen biotech; 41420ES76; Shanghai, China) supplemented with 10% heat-inactivated fetal bovine serum (FBS, Yeasen biotech; 40130ES76; Shanghai, China). All cell lines were validated by STR profiling and tested negative for mycoplasma. Cells were all cultured in a humidified incubator at 37 °C and 5% CO${}_2$. Chemically synthesized control siRNA and two siRNAs targeting PTTG1 were obtained from RiboBio, China. For RNAi transfection, RNAiMAX (Thermo Fisher Scientific;13778150; Waltham; MA; USA) was administered at cell confluences from 30% to 40%. Following transfection, cells were harvested within 48 to 72 hours for subsequent experiments. The target sequences are provided below:

PTTG1-1: 5${}^{\prime }$-GAAGACTGTTAAAGCAAAA-3${}^{\prime }$

PTTG1-2: 5${}^{\prime }$-GATGATGCCTATCCAGAAA-3${}^{\prime }$

Cell proliferation was evaluated utilizing the Cell Counting kit-8 (CCK-8, Yeasen biotech; 40203ES60; Shanghai, China) assay from Yeasen. In 96-well plates, cells were seeded at a density of 6 $\times$ 10${}^3$ cells per well. Once the cells had adhered to the well, CCK-8 assays were conducted daily for four consecutive days in accordance with the manufacturer’s instructions. The absorbance was measured at 450 nm and 630 nm per well using a microplate reader (A51119500C; Thermo Fisher Scientific; Multiskan Spectrum; Waltham; MA; USA).

To identify EdU-positive cells, flow cytometry analysis was performed on proliferating cells using the Cell-Light EdU Apollo 488 (Yeasen biotech; 40275ES76; Shanghai, China), following the manufacturer’s protocol. The fluorescence signal was detected at 488 nm using flow cytometry.

To assess cell apoptosis, SK-N-SH cells were seeded in 6-well plates and transfected with siPTTG1 when they reached a confluency of 30%–40%. After 48 hours, an Annexin V-FITC kit (Yeasen biotech; 40306ES60; Shanghai, China) was utilized.

Western blotting was performed following previously described protocols [35]. The primary antibodies targeting PTTG1 (Abcam; ab79546, 1:100, Univ Cambridge UK) were sourced from Abcam, while $\beta$-actin was obtained from Cell Signaling Technology (CST; 3700S; 1:1000; Boston, MA, USA).

RNA extraction was performed using TRIzol reagent (Yeasen biotech; 19202ES60; Shanghai, China), and reverse transcription was conducted using PrimeScript RT Master Mix (Perfect Real Time) kit (Yeasen biotech; 10137ES76; Shanghai, China). Real-time quantitative PCR (qPCR) was performed using SYBR Green Master Mix (Yeasen biotech; 11201ES60; Shanghai, China). Relative mRNA expression levels were determined using the 2${}^{-\mathrm{\Delta }\mathrm{\Delta }\text{Cq}}$ method [35]. GENEWIZ produced specific primers with the following sequences:

PTTG1 forward: 5${}^{\prime }$-GCTTTGGGAACTGTCAACAGAGC-3${}^{\prime }$

PTTG1 reverse: 5${}^{\prime }$-CTGGATAGGCATCATCTGAGGC-3${}^{\prime }$

GAPDH forward: 5${}^{\prime }$-GTCTCCTCTGACTTCAACAGCG-3${}^{\prime }$

GAPDH reverse: 5${}^{\prime }$-ACCACCCTGTTGCTGTAGCCAA-3${}^{\prime }$

In this investigation, disparities in gene expression were assessed through the Wilcoxon rank sum test and the Kruskal–Wallis test. Additionally, we gauged the correlation between the two groups using either Spearman’s rank correlation coefficient or Pearson correlation analysis. Survival characteristics were compared using the Kaplan–Meier method and Cox regression analysis. Statistical analyses and visualizations were executed using R software (R-4.2.2; University of Auckland, New Zealand), Sangerbox, and GraphPad Prism 9 (GraphPad Software Inc.; California, USA) [36]. A p-value 0.05 was considered statistically significant. Significance levels are as follows: *p 0.05, **p 0.01, ***p 0.001, and ****p 0.0001, as indicated in the figures and figure legends.

First, we present a flowchart of the analytical methods used in this paper (Supplementary Fig. 1). To determine the cellular localization of PTTG1, we employed indirect immunofluorescence to examine its distribution in the endoplasmic reticulum (ER) and microtubules of PC-3 prostatic cancer (PC) cells, U-251 malignant glioblastoma (MG) cells, and U-2 osteosarcoma (OS) cells, all sourced from the HPA database. Our findings indicate that PTTG1 is predominantly localized in the cytoplasm of all three cell types, evident from its co-localization with cytoplasmic markers (Fig. 1A). Moreover, the structural arrangement of the PTTG1 protein suggests that natural missense variants of Ala2 and Ser165 are situated within the cell membrane (Fig. 1B). In our investigation, the analysis of single-cell RNA-sequencing data, utilizing fluorescent ubiquitin-based technology, demonstrated a substantial correlation between increased expression of PTTG1 RNA and cell cycle progression. Changes in protein expression levels of PTTG1 were observed to align with progression through the G1, S, and G2 phases (Fig. 1C). Furthermore, our research revealed the presence of PTTG1 messenger RNA (mRNA) and protein in various normal human tissues, including the testis, lymph nodes, tonsils, thymus, nasopharynx, and thyroid gland (Fig. 1D,E).

Fig. 1.

Pituitary tumor-transforming gene 1 (PTTG1) variants, localization, single-cell variations, functional partners, and expression profiles under physiological conditions. (A) Immunofluorescence staining demonstrates the subcellular distribution of PTTG1 within the nucleus, endoplasmic reticulum (ER), and microtubules of PC-3 prostatic cancer cells, U-251 malignant glioblastoma (MG) cells, and U-2 osteosarcoma (OS) cells, sourced from the Human Protein Atlas (HPA) database. (B) The topology of the PTTG1 protein reveals its membrane localization, featuring a natural missense variant of Ala2 and Ser165. (C) A plot based on single-cell RNA-Seq data illustrates the correlation between PTTG1 mRNA expression and cell cycle progression. (D) Expression levels of PTTG1 RNA in various tissues were assessed using the HPA dataset. (E) The expression level of PTTG1 protein in tissues was evaluated in the HPA dataset (https://www.proteinatlas.org/ENSG00000164611-PTTG1/tissue). RNA-Seq, RNA sequencing; mRNA, messenger RNA.

In this investigation, we utilized R software to assess the expression differences between normal and tumor samples across various cancers. Significance analysis was conducted using unpaired Wilcoxon rank-sum and signed-rank Tests. PTTG1 exhibited significant upregulation in 33 tumors, encompassing GBM, GBMLGG, LGG, UCEC, BRCA, CESC, LUAD, ESCA, STES, KIRP, KIPAN, COAD, COADREAD, PRAD, STAD, KIRC, LUSC, LIHC, WT, SKCM, BLCA, THCA, READ, OV, PAAD, UCS, ALL, LAML, PCPG, ACC, KICH, and CHOL (Fig. 2A).

Fig. 2.

Examining the correlation between PTTG1 gene expression and diverse clinicopathological stages in pan-cancer. (A) Comparative analysis of PTTG1 expression in TCGA and GTEx databases. (B) Violin plots depict the variation in PTTG1 expression across pathological G1/G2/G3 stages. (C) Violin plots display the differences in PTTG1 expression levels among pathological I/II/III/IV stages. (D) Violin plots illustrate the distinctions in PTTG1 expression levels across pathological T1/T2/T3/T4 stages. Significance denoted as *p 0.05, **p 0.01, ***p 0.001 and ****p 0.0001. TCGA, The Cancer Genome Atlas; GTEx, The Genotype-Tissue Expression.

Moreover, the expression of PTTG1 demonstrated significant variations among patients with GBMLGG, LGG, STES, KIPAN, UCEC, HNSC, KIRC, LIHC, and PAAD, contingent on their pathological stage (G1/G2/G3/G4) (Fig. 2B). In several cancers, including LUAD, BRCA, KIRP, KIPAN, KIRC, LUSC, LIHC, TGCT, UCS, ACC, KICH, and DLBC, PTTG1 expression exhibited notable differences across pathological stages I, II, III, and IV (Fig. 2C). Additionally, there were significant differences in PTTG1 expression among patients with BRCA, KIRP, KIPAN, PRAD, KIRC, LUSC, LIHC, TGCT, and ACC, based on their pathological T1/T2/T3/T4 stage (Fig. 2D).

A comprehensive analysis of PTTG1 in malignant tumors was undertaken to elucidate its role in cancer. The TCGA pan-cancer dataset was examined, revealing that the most prevalent DNA alteration in PTTG1 was amplification. This amplification was most frequently observed in kidney renal clear cell carcinomas, followed by cholangiocarcinoma and ovarian serous cystadenocarcinoma (Fig. 3A). Notably, PTTG1 mutations were identified in uterine corpus endometrial carcinoma, stomach adenocarcinoma, and colorectal adenocarcinoma, with deep deletion of PTTG1 predominantly observed in stomach adenocarcinoma. Furthermore, a statistically significant correlation was observed between PTTG1 expression and mutation type and copy number (Fig. 3B). Missense mutations were identified as the primary genetic alterations in the PTTG1 gene, with the P164Lfs4 mutation being the most common alteration, observed in two cases of UCEC (Fig. 3C). The three-dimensional structure of the PTTG1 protein further highlighted the P164Lfs4 site (Fig. 3D). Therefore, the elevated expression of PTTG1 in cancer may be attributed to genetic variations. Recent research has identified over 100 types of RNA modifications that act as crucial regulators of gene transcriptional expression. RNA methylation and its associated downstream signaling pathways are pivotal in various biological processes, including cell differentiation, sex determination, and stress responses [37]. The expression of PTTG1 is believed to be primarily regulated through RNA post-transcriptional modifications, evident from the significant positive correlation between PTTG1 expression and RNA modification-related genes, such as m1A, m5C, and m6A. Specifically, genes involved in RNA modification, including METTL3, METTL14, WTAP, and YTHDF1, exhibited a significant positive correlation with PTTG1 expression (Fig. 3E).

Fig. 3.

Mutation characteristics of PTTG1 in various TCGA tumors. (A) Utilizing the cBioPortal tool, we scrutinized the mutation features of PTTG1 across TCGA tumors, presenting the alteration frequency categorized by mutation type. (B) Depiction of potential alterations in PTTG1 copy number. (C) Visualization of the PTTG1 3D structure, highlighting the mutation site with the highest alteration frequency (P164Lfs*4 site). (D) Presentation of the specific mutation site. (E) Evaluation of the correlation between PTTG1 expression and the expression of RNA modification regulators in pan-cancer. Significance indicated as *p 0.05.

The survival analysis of PTTG1 across four domains—OS, DSS, PFS, and DFS—reveals its prognostic significance in pan-cancer. The Cox regression model analysis indicates that patients with elevated PTTG1 expression in the 16 considered tumor types face an increased risk of poor overall survival (OS) (Fig. 4A), encompassing GBMLGG, LGG, LAML, LUAD, KIRP, KIPAN, KIRC, LIHC, MESO, UVM, PAAD, ALL, CHOL, ACC, KICH, and ALL-R. Subsequent investigations demonstrate a substantial association between PTTG1 expression and DSS across various carcinoma categories, including but not limited to GBMLGG, KIPAN, KIRP, KIRC, LGG, KICH, ACC, MESO, LIHC, LUAD, PAAD, BRCA, and UVM (Fig. 4B). Moreover, heightened PTTG1 expression suggests a lower DFI in BRCA, SARC, KIRP, KIPAN, and LIHC (Fig. 4C). We further established a univariate Cox regression model to correlate PTTG1 expression with PFI in various cancer types. The findings highlight that PTTG1 expression is associated with poor prognosis in 16 tumor types, including GBMLGG, KIPAN, KIRP, KIRC, LGG, PRAD, LIHC, KICH, ACC, UVM, MESO, BRCA, SARC, LUAD, PAAD, and PCPG (Fig. 4D). The associations between PTTG1 and OS, DSS, DFS, and PFS were evaluated using Kaplan–Meier (KM) survival analysis curves, and revealed a trend consistent with the univariate Cox regression analysis. In CESC, GBMLGG, KIRP, LAML, LGG, and LIHC, elevated PTTG1 expression correlated with a declining trend in OS (Supplementary Fig. 2), mirroring the KM curves for DSS in GBMLGG, KIPAN, KIRP, and LGG (Supplementary Fig. 3A–D). Kaplan–Meier survival analysis further demonstrated a significant correlation between high PTTG1 expression and disease-free survival (DFS) in breast invasive carcinoma (BRCA), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), and pancreatic adenocarcinoma (PAAD) (Supplementary Fig. 3E–H). PTTG1 exhibited a robust association with progression-free survival (PFS) across pan-cancer studies, notably showing low PFS rates in patients with GBMLGG, KIRC, LGG, and PRAD (Supplementary Fig. 4). To substantiate their prognostic value, separate nomogram models were constructed for KIRC, LUSC, and LIHC, chosen based on univariate Cox regression results. The accuracy of these models was assessed through calibration curves at 1-year, 3-year, and 5-year intervals. The findings demonstrated that the nomogram models for KIRC (Supplementary Fig. 5A), LUSC (Supplementary Fig. 5C), and LIHC (Supplementary Fig. 5E) accurately predicted survival rates, emphasizing the significant impact of PTTG1 on prognosis and its role as a robust predictor of overall survival over the 1-year, 3-year, and 5-year periods (Supplementary Fig. 5B,D,F).

Fig. 4.

Univariate Cox Regression Analysis of PTTG1 Regarding OS, PFS, DFS, and DSS Across Pan-Cancer. (A) Assessment of the correlation between PTTG1 expression and OS (overall survival). (B) Examination of the correlation between PTTG1 expression and DSS (disease-specific survival). (C) Investigation of the correlation between PTTG1 expression and DFI (disease-free interval). (D) Evaluation of the correlation between PTTG1 expression and PFI (progression-free interval).

This study aimed to explore the influence of PTTG1 on the tumor microenvironment (TME) and its association with immune infiltration levels across various cancers. Additionally, we investigated the correlation between PTTG1 expression and three immune scores. Our findings indicated a significant negative correlation between PTTG1 expression and immune infiltration, as assessed by StromalScore, in BRCA, STES, STAD, and LUSC (Fig. 5A). Similarly, there was a negative correlation between PTTG1 expression and immune infiltration, as determined by ImmuneScore, in GBM, STES, LUSC, and READ (Fig. 5B). EstimateScore suggested a decreasing trend in immune infiltration levels with elevated PTTG1 expression in GBM, STES, STAD, and LUSC (Fig. 5C). Despite variations in score values, the overall trend remained consistent, highlighting the significant regulatory role of PTTG1 in the tumor immune microenvironment across the mentioned malignancies. Our analysis explored the potential correlation between PTTG1 expression and immune checkpoint pathway genes across diverse cancer types. The results demonstrated a significant association between PTTG1 and both immunosuppressive and immunostimulatory genes in pan-cancer. Of particular note was the positive correlation observed between PTTG1 and the immune checkpoint pathway genes, providing further evidence of the involvement of PTTG1 in the regulation of the immune response in cancer (Fig. 5D). We conducted a comparative analysis of PTTG1 with genes related to immune regulation, including major histocompatibility complex (MHC), chemokines and chemokine receptor genes, immunosuppressants, and immunostimulators. Our findings revealed strong correlations between PTTG1 expression levels and these three gene families across various tumor types. The correlation analysis of PTTG1 with immune checkpoint genes showed a positive correlation in GBMLGG, LGG, KIRP, and KIPAN, while a negative correlation was observed in DLBC and THYM. In several malignancies, such as GBMLGG, LGG, KIPAN, KIRC, OV, UVM, MESO, LIHC, PAAD, THYM, and DLBC, the correlation between PTTG1 and the five gene families was particularly significant, demonstrating an overall positive correlation. Notably, GBMLGG, LGG, KIPAN, and KIRC exhibited the highest expression levels, and PTTG1 expression positively correlated with nearly all MHC and chemokine receptor genes in these five malignancies. While this correlation was evident in pan-cancer, it was not statistically significant in UCS, SKCM, and WT tumors (Fig. 5E).

Fig. 5.

The relationships between PTTG1 expression and immune infiltration and immune checkpoints. (A) Examining the correlation between PTTG1 expression and StromalScore. (B) Investigating the correlation between PTTG1 expression and ImmuneScore. (C) Assessing the correlation between PTTG1 expression and EstimateScore. (D) Analyzing the correlation between PTTG1 expression and genes related to immune checkpoints. (E) Exploring the correlation between PTTG1 and genes involved in immunomodulation. Significance denoted as *p 0.05.

We evaluated tumor immune scores using four algorithms: xCELL, CIBERSORT, QUANTISEQ, and EPIC. Moreover, we explored the correlation between PTTG1 expression and immune cell levels through immune infiltration analysis. Our findings revealed a significant enhancement in immune cell infiltration in most tumors with high PTTG1 expression, which particularly impacted macrophages, Th1 cells, Th2 cells, and T cells (Fig. 6A). Combining results from the three algorithms indicated that a negative correlation existed between PTTG1 and immune cell infiltration in various malignancies, such as OV, TGCT, GBM, KIRC, and SARC, suggesting that PTTG1 was involved in shaping an immunosuppressive microenvironment in these cancers. Comparisons across algorithms showed strong positive or significant negative correlations of PTTG1 with immune cells in KIPAN, KIRC, PRAD, BRCA, LGG, and others, highlighting the intricate relationship between PTTG1 and these immune cells. Notably, PTTG1 significantly promoted immune cell infiltration in most cancers, except for CHOL, PCPG, and other specific cancer types (Fig. 6B; Supplementary Fig. 6A,B). Additionally, we examined PTTG1 expression in TME-related cells using the scRNA-seq database. Across AEL, AML, ALL, BCC, BRCA, CHOL, CRC, and Glioma, CD4Tconv, CD8T, CD8Tex, Mono/Macro, Treg or macrophages, and proliferating T cell fibroblasts exhibited the highest PTTG1 expression levels (Supplementary Fig. 7A–H). These results underscored the close association of PTTG1 with the tumor microenvironment in cancer.

Fig. 6.

Correlation between PTTG1 and the level of immune infiltrating Cells. (A) The association between PTTG1 and the level of immune infiltrating cells in cancers was analyzed using xCELL algorithms. (B) The correlation between PTTG1 and the level of immune infiltrating cells in cancers was analyzed using CIBERSORT algorithms. Asterisks denote statistically significant p-values calculated through Spearman’s correlation analysis (*p 0.05).

Checkpoint blockade (ICB) therapy, utilizing drugs, such as anti-programmed cell death protein 1 (PD-1), anti-programmed death-ligand 1 (PD-L1), and/or anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) [38, 39, 40], has demonstrated promising outcomes in a subset of cancer patients. Tumor mutational burden (TMB) has emerged as a pivotal biomarker for predicting responses to various cancers. Elevated TMB levels have been correlated with favorable responses to immune checkpoint blockade (ICB) therapy, establishing it as a crucial tool for identifying patients likely to benefit from such treatments [41]. Microsatellite instability (MSI), a diagnostic phenotype observed in gastrointestinal neoplasms, manifests as the spontaneous loss or gain of nucleotides in repetitive DNA strands [42]. This study delved into the relationship between PTTG1 expression, TMB, and MSI across diverse cancer types. In most cases, results unveiled a predominantly positive correlation between PTTG1 expression and TMB/MSI. Notably, in BRCA, GBMLGG, LGG, PRAD, LUAD, STES, and STAD, the strongest correlation surfaced between PTTG1 and TMB score (Fig. 7A). Conversely, the association between PTTG1 and MSI was more pronounced in patients with DLBC, STAD, COAD, GBMLGG, and STEC (Fig. 7B). The biology of solid tumors is significantly shaped by tumor purity, a factor that also influences the efficacy of immune checkpoint inhibitor (ICI) therapy [43], and PTTG1 displayed a significant correlation with purity, which was more prominent in THYM, KIPAN, KIRC, GBM, DLBC, UCEC and others, with some similarity to TMB and MSI (Fig. 7C). The correlation between stemness scores, drug resistance, and tumor cell proliferation during the treatment of malignant tumors has been explored. The prognostic significance of mRNA-based stem index (mRNAsi) in different cancers has been previously documented [44, 45, 46]. To delve deeper into the connection between PTTG1 expression and stemness scores across various tumors, we conducted a Pearson correlation analysis. Our findings revealed a robust correlation between PTTG1 expression and stemness scores in THYM, GBMLGG, LGG, GBM, UVM, THCA, and ssDNA (Fig. 7D).

Fig. 7.

Prediction of treatment response to immune checkpoint inhibitors (ICIS) by PTTG1. (A) Correlation analysis examining the relationship between PTTG1 expression and tumor mutational burden (TMB). (B) Correlation analysis investigating the association between PTTG1 expression and microsatellite instability (MSI). (C) Correlation analysis assessing the connection between PTTG1 expression and tumor purity. (D) Correlation analysis of the relationship between PTTG1 expression and stemness score.

In 2015, Noll et al. [47] reported an association between PTTG1 expression, hyperproliferation, and poor prognosis in multiple myeloma. Cho-Rok et al. [48], in 2006, demonstrated that adenovirus-mediated reduction of PTTG1 expression using siRNA inhibits the growth of liver cancer cells both in vitro and in vivo. Li et al. [49] revealed in 2013 that PTTG1 promotes the migration and invasion of human non-small cell lung cancer cells. Furthermore, Yoon et al. [19], in 2012, uncovered that the PTTG1 oncogene fosters tumor malignancy by inducing epithelial–mesenchymal transition and expanding the cancer stem cell population. Additionally, PTTG1 influences cell cycle arrest in breast cancer cells, whereby its overexpression results in a higher proportion of breast cancer cells in the S phase [50]. To substantiate the role of PTTG1, we employed the neuroblastoma cell line (SK-N-SH). Additionally, we conducted experiments after validating the interference efficiency through siRNA, confirmed by qPCR and Western blot analysis (Supplementary Fig. 8A,B). The CCK-8 assay was employed to monitor cell proliferation at 24-, 48-, 72-, and 96-hours post-transfection with siPTTG1. Our findings revealed that reducing PTTG1 expression in neuroblastoma cell lines significantly decreased cell viability, suggesting that inhibiting PTTG1 expression can impede the proliferation of neuroblastoma cells (Supplementary Fig. 8C). Results from the EdU experiment demonstrated a substantial decrease in the percentage of positive signals of EdU-labeled cells when PTTG1 expression was reduced. This reduction in positive signals indicates a decrease in the DNA replication activity of tumor cells and a weakening of their cell proliferation ability (Supplementary Fig. 8D). Apoptosis analysis revealed a significant increase in the average percentage of apoptotic cells following the knockdown of the PTTG1 gene (Supplementary Fig. 8E). Collectively, our results provide additional evidence supporting the wide-ranging tumorigenic effects of PTTG1 on multiple tumor types.