Multi-omics analysis Identifies PTTG1 as a prognostic biomarker associated with immunotherapy and chemotherapy resistance

Multi-omics analysis Identifies PTTG1 as a prognostic biomarker associated with immunotherapy and chemotherapy resistance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-omics analysis Identifies PTTG1 as a prognostic biomarker associated with immunotherapy and chemotherapy resistance Handong Wei, Yaxin Ma, Shuxing Chen, Chunlin Zou, Lihui Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4923978/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Oct, 2024 Read the published version in BMC Cancer → Version 1 posted 4 You are reading this latest preprint version Abstract Background Pituitary tumor-transforming gene 1 (PTTG1) is an important gene in tumour development. However, the relevance of PTTG1 in tumour prognosis, immunotherapy response, and medication sensitivity in human pan-cancer has to be determined. Methods TIMER, GEPIA, the human protein atlas, GEPIA, TISCH2, and cBioportal examined the gene expression, protein expression, prognostic value, and genetic modification landscape of PTTG1 in 33 malignancies based on the TCGA cohort. The association between PTTG1 and tumour immunity, tumour microenvironment, immunotherapy response, and anticancer drug sensitivity was investigated using GSCA, TIDE, and CellMiner CDB. Molecular docking was used to validate the possible chemotherapeutic medicines for PTTG1. Additionally, siRNA-mediated knockdown was employed to confirm the probable role of PTTG1 in paclitaxel-resistant cells. Results PTTG1 is overexpressed and associated with poor survival in most tumors. Functional enrichment study revealed that PTTG1 is involved in the cell cycle and DNA replication. A substantial connection between PTTG1 expression and immune cell infiltration points to PTTG1's possible role in the tumour microenvironment. High PTTG1 expression is associated with tumour immunotherapy resistance. The process could be connected to PTTG1, which mediates T cell exhaustion and promotes cytotoxic T lymphocyte malfunction. Furthermore, PTTG1 was found to be substantially linked with sensitivity to several anticancer medications. Suppressing PTTG1 with siRNA reduced clone formation and migration, implying that PTTG1 may play a role in paclitaxel resistance. Conclusion PTTG1 shows potential as a cancer diagnostic, prognostic, and chemosensitivity marker. Increased PTTG1 expression is linked to resistance to cancer treatment. The mechanism could be linked to PTTG1's role in promoting cytotoxic T lymphocyte dysfunction and mediating T cell exhaustion. It is feasible to consider PTTG1, which is expressed on Treg and Tprolif cells, as a new therapeutic target for overcoming immunotherapy resistance. PTTG1 Tumor immunity Immunotherapy Drug sensitivity Pan-cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction The pituitary tumor-transforming gene 1 (PTTG1), also known as SECURIN was isolated from pituitary tumors and identified as a gene with tumorigenic effect in vivo. It encodes a homolog of the yeast Securin protein and acts as a sister chromatid regulator that inhibits separase function and thus prevents sister chromatid separation. 1 , 2 . As a transcriptional activator, PTTG1 can activate C-Myc and up-regulate the expression of cyclinB1 and CDK1 3 . PTTG1 plays a role in chromosomal stability maintenance, orderly cell cycle monitoring, genetic stability and mitotic fidelity assurance, malignant transformation, tumour induction, invasion, and metastasis 4 , 5 . Research studies indicate that overexpression of PTTG1 is associated with unfavorable tumor phenotype and adverse prognosis. This shows that PTTG1 plays a critical role in the occurrence and progression of tumours. For instance, hepatocellular carcinoma has an elevated overexpression of PTTG1, which enhances the synthesis of asparagine to activate mTOR and advance the tumour 6 . Furthermore, it was shown that a higher level of PTTG1 was associated with poorer prognosis for LUAD patients 7 , and targeted PTTG1 inhibition improves radiation-induced antitumour immunity in lung adenocarcinoma 8 . Additionally, the predictive values of PTTG1 for immunotherapy response 8 and chemosensitivity 9 were also revealed. Although the importance of PTTG1 in tumors is becoming more widely known, there have been no comprehensive studies of its function in tumor occurrence, recurrence, immunotherapy or chemotherapy. In this work, we evaluated the gene expression, prognostic value, and relate to immunotherapy response of PTTG1 in many well-established databases. We discovered that PTTG1 is overexpressed in several tumours and is related with poor survival in a variety of malignancies. PTTG1 promotes cytotoxic T cell malfunction, which contributes to immunotherapy resistance. PTTG1 was found to be significantly related with sensitivity to several anticancer medicines, particularly MET inhibitors. The current findings highlighted the critical function of PTTG1 in carcinogenesis, immunotherapy, and chemotherapy response, identifying a new target for cancer treatment. Materials and methods Gene and protein expression analysis The differential expression of PTTG1 in tumor and neighboring normal tissues across all TCGA tumors was analyzed by TIMER (Tumour Immune Estimation Resource, http://timer.cistrome.org/ ) 10 . The Wilcoxon test was used to determine the statistical significance of differential expression. The PTTG1 gene expression profile and pathological stage plot across all tumor samples and paired normal tissues were analyzed by GEPIA2 (Gene Expression Profiling Interactive Analysis 2, http://gepia2.cancer-pku.cn/#index ) 11 . The immunohistochemistry-based protein expression profiles of PTTG1 were analyzed by the Human Protein Atlas (hpa, https://www.proteinatlas.org/ ) 12 . Kaplan–Meier Test Survival analysis of PTTG1 in tumor patients was obtained from the GEPIA2 based on the TCGA datasets. Kaplan-Meier Plotter 13 ( http://kmplot.com/analysis/ ) was used to validate the prognostic value of PTTG1 in NSCLC patients. The Kaplan-Meier method was performed to plot the overall survival (OS) or disease-free survival (DFS, also called relapse-free survival and RFS) curves. Survival differences were assessed by the log-rank test using the median of PTTG1 as a cutoff value. The cox proportional hazard ratio (HR) with 95% confidence intervals and log‑rank P ‑values were calculated. Genetic alteration analysis The PTTG1 alteration landscape in human cancers was depicted based on TCGA pan-cancer atlas studies by cBioPortal ( https://www.cbioportal.org/ ) 14 . To determine the correlation with copy number variation and survival, patients were divided into delete, amplification, and wild-type groups based on PTTG1 mutation type. The Kaplan-Meier method was performed to plot the overall survival curves in TCGA datasets by GSCA (Gene Set Cancer Analysis, https://guolab.wchscu.cn/GSCA/#/ ) 15 . Similar Genes Detection and PPI network construction To identify genes with a similar expression pattern to PTTG1 in pan-cancer, the Pearson correlation coefficients were analyzed using the GEPIA2 database. The similar genes were analyzed using the STRING database ( https://string-db.org/ ) to generate the PPI network. PPI pairs with a combined score > 0.4 were extracted. The PPI network was visualized using Cytoscape 3.7.2 software 16 , and the most important module was performed using the MCODE plug-in in Cytoscape software. The Metascape web-based tool ( https://metascape.org/gp/index.html ) 17 was used for functional enrichment analysis (GO analysis and KEGG pathway analysis) of these genes. PTTG1 and tumor immune microenvironment The correlation of PTTG1 expression with immune infiltration level in diverse cancer types was analyzed by TIMER (Tumor IMmune Estimation Resource, https://cistrome.shinyapps.io/timer/ ) 10 . The TCGA pan-cancer data set was retrieved from the UCSC database. The PTTG1 expression data in individual samples was retrieved, and a log2(x + 1) transformation was applied. The immune, stromal, and ESTIMATE scores for each sample were determined using the R package ESTIMATE ( https://bioinformatics.mdanderson.org/estimate/rpackage.html ). Pearson correlation analysis was used to identify significantly linked immune infiltration scores. PTTG1 and immunotherapy response TIDE (Tumor Immune Dysfunction and Exclusion, http://tide.dfci.harvard.edu/ ) is a computational framework created to assess the potential of tumour immune escape from the gene expression profiles of cancer samples 18 . The 'Biomarker Evaluation' module was used to compare PTTG1 to other reported biomarkers in terms of their predictive ability for response outcome and overall survival. The 'Query Gene' module calculated the PTTG1 gene signature in T-cell dysfunction using data from the TCGA, PRECOG, and METABRIC databases. The T cell dysfunction score of PTTG1 is defined as the Wald test z score 18 . The two-sided Wald test was used to calculate the relationship between cytotoxic T lymphocyte CTL levels and overall survival. The Kaplan-Meier plot divides tumours into two groups: 'High CTL' has above-average CTL values across all samples, and 'Low CTL' has values below average. To demonstrate the relationship between CTL levels and survival outcomes, samples were divided based on their PTTG1 expression levels. Expression level of PTTG1 at the single‑cell level TISCH2 (Tumor Immune Single-cell Hub 2, http://tisch.comp-genomics.org/ ) is a scRNA-seq database that focusses on the tumour microenvironment. It provides precise cell-type annotation at the single-cell level, allowing the analysis of TME across various cancer types 19 . The expression level of PTTG1 at the single-cell level was visualized by TISCH2 in the NSCLC_GSE139555, KIRC_GSE111360, and LIHC_GSE140228 datasets. The PTTG1 expression between immunotherapy responders and non-responders was investigated in the LIHC_GSE125449_aPDL1aCTLA4 dataset. PTTG1 and chemo response CellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb) 20 enables the analysis of molecular and pharmacological data across cancer cell line databases to identify medications that match genomic determinants of response. CellMinerCDB investigated the relationship between PTTG1 expression and chemotherapy response using the GDSC (Sanger/Massachusetts General Hospital Genomics of Drug Sensitivity in Cancer) dataset. Moleular Docking The X-ray crystal structure of PTTG1 (7nj0) was obtained from the RCSB PDB protein databank ( http://www.rcsb.org/ ). The 3D formats of ABT-263 (ZINC150338726) and NSC-207895 (ZINC5180959) were obtained from ZINC15 drug database ( http://zinc15.docking.org/ ) 21 . Discovery Studio 4.5 Client deleted the water and ligand molecules from the crystal structures before starting the docking simulation. PyRx software was used to simulate drug docking and PTTG1 interactions. We calculated interaction energies to predict docking positions and pick the binding pose with the lowest binding energy (kcal mol − 1 ). The results were visualised and analysed with Discovery Studio 4.5 Client. Cell lines Human lung cancer cell lines A549, H358 and the paclitaxel resistant cell lines A549-TXR, H358-TXR were kindly provided by Professor Wang Luo (University of Michigan, USA). Cells were maintained in RPMI 1640 or F12K supplemented with 10% FBS and 1% antibiotic-antimycotic. All cell lines were cultured at 37°C in a 5% CO2 cell culture incubator. Mycoplasma contamination was excluded in these cell lines. Real-Time Quantitative PCR Total RNA was reverse transcribed into cDNA using Reverse Transcription Kit (Thermo Fisher Scientific). qRT-PCR was performed using Power SYBR™ Green (Thermo Fisher Scientific) and ABI 7300 detection system (Applied Biosystems). qRT-PCR data were normalized to the expression of housekeeping gene GAPDH, and relative expressions were calculated using the 2 −ΔΔCt method. The oligonucleotide primers were shown as follows: PTTG1 (forward: 5’- ATGAATGCGGCTGTTAAGACCTG-3’, reverse: 5’- TCCCATCTAAGGCTTTGATTGAAGG-3’). All tests were performed in triplicate, and the data were presented as mean ± SD. siRNA-mediated knockdown Cells were seeded at the desired concentration in 60 mm plates and then transfected with 10 nM experimental siRNA oligonucleotides or non-targeting controls 24 hours later. Lipofectamine® RNAiMax Reagent (Invitrogen, USA) was used to knockdown cells in OptiMEM medium, following the manufacturer's recommendations. The knockdown efficiency was measured using qPCR. PTTG1 siRNA sequences for knockdown tests are listed below: sense: 5'-UGUGGUUGCUAAGGAUGGGCUTT-3'; antisense: 5'- AGCCCAUCCUUAGCAACCACATT-3'. Clone Formation Assay A549-TXR and H358-TXR cells (250 per well) were planted in 12-well plates and grown overnight. Cells were transfected with 10 nM experimental siRNA oligonucleotides or non-targeting controls 24 hours after plating. After two weeks, the cells were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Each well's clone count was recorded. Basement membrane migration assays Cells were treated with the siRNAs listed above to perform invasion tests. After 48 hours of transfection, cells were trypsinised, and diluted to the desired concentration. Cells were seeded into basement membrane matrix Boyden chambers (8-mm pore size, BD) located in the insert of a 24-well culture plate. The lower compartment received 20% FBS as a chemoattractant. After 48 hours, the non-migrating cells were gently removed using a cotton swab. Cells on the chamber's lower side were stained with Diff-QuikTM Stain Set (SIEMENS), then air dried and photographed. Statistical analysis ANOVA was used to compare PTTG1 expression levels in tumour and normal samples from the TCGA and GTEx datasets. Pearson's correlation coefficient was used to analyse the correlation between Immuno Score, Stromal Score, and ESTIMATE Score, as well as immunological checkpoints. The P -value < 0.05 was judged significant. Results PTTG1 is overexpressed in pan cancer and associated with tumor stages We performed a gene expression examination for PTTG1 on the gene expression matrix derived from the TCGA datasets using TIMER. PTTG1 mRNA expression was considerably higher in BLCA, BRCA, CHOL, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, STAD, and UCEC, but significantly lower in THCA. The remaining cancer types exhibited no significant variation in PTTG1 mRNA levels between normal and tumor cells (Fig. 1 A). The PTTG1 mRNA expression in pan-cancer was then analyzed using RNA sequencing datasets from the TCGA and GTEx projects by GEPIA. In addition to the 14 cancer types listed above, PTTG1 was significantly overexpressed in ACC, CESC, DLBC, GBM, OV, PAAD, SKCM, THYM, and UCS while being underexpressed in LAML and TGCT (Fig. 1 B). To validate the diagnostic and prognostic relevance of PTTG1 in pan cancer, we used GEPIA to investigate its expression in tumor pathological stages. The findings revealed that PTTG1 was overexpressed in samples from advanced cancer patients with KIRC, KIRP, ACC, LUAD, LIHC, and BRCA (Fig. 1 C). We then obtained the immunohistochemistry staining of PTTG1 from the HPA database. The PTTG1 protein stained darker in LUAD, BRCA, LIHC, and KIRC samples (Fig. 1 D). The findings indicate that the PTTG1 protein may be significantly expressed in these tumors. PTTG1 is a predictor of poor patient survival To investigate the prognostic benefit of PTTG1, we compared survival (OS and DFS) differences between high and low PTTG1 expression groups in pan-cancer. The high PTTG1 expression group had substantially lower overall survival (P < 0.05) compared to the low expression group in ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, THCA, and UVM (Fig. 2 A). Increased PTTG1 expression was linked to decreased disease-free survival in ACC, KIRC, KIRP, LGG, LIHC, MESO, PAAD, PRAD, SARC, and UVM (Fig. 2 B). These findings indicate that PTTG1 is overexpressed and linked with poor outcomes in ACC, KIRC, KIRP, LIHC, LUAD, and PAAD, and has the potential to be a predictive biomarker. Associations of PTTG1 expression with genomic alterations in pan-cancer cBioPortal was used to analyse genetic variations (mutation, structural variant, amplification, and deep deletion) in PTTG1 across malignancies. In the majority of cancer types, PTTG1 gene amplification was obvious. High amplification was observed in KIRC, CHOL, and UCS (Supplementary Figs. 1A, B). The GSCA database was then used to analyse the frequency of change in single nucleotide variation (SNV) and copy number variation (CNV) in the PTTG1 gene. The findings revealed that SNV of PTTG1 is more frequently found in UCEC (0.88%, Fig. 3 B) and STAD (0.68%, Fig. 3 B), with low frequency in the other cancer types (Fig. 3 A). The most common detrimental mutation (Missense_Mutation, Splice_Site, Frame_Shift_Del, etc.) in the PTTG1 gene is missense mutation. Point mutation analysis revealed that the SNV of PTTG1 had C > T and C > A transversions (Fig. 3 C). Next, we looked for CNV of the PTTG1 gene across malignancies. The findings revealed that CNV of PTTG1 was common in KIRC, ACC, CHOL, BLCA, TGCT, and LUSC, but uncommon in THCA, LAML, and PRAD (Fig. 3 D). We investigated at the Kaplan-Meier survival curves for PTTG1 CNV (homo amplification and homo deletion) and broad type (Fig. 3 E). KIRP patients with homo deletion and homo amplification of PTTG1 genes exhibited a poor prognosis compared to the wild type group (P < 0.05, Fig. 3 F). Protein-protein interactions of PTTG1 and similar genes in pan-cancer In order to investigate the probable mechanism of PTTG1 in carcinogenesis, we identified the top 100 genes with similar expression patterns to PTTG1 in the TCGA cancer cohort using GEPIA2. The top five genes with similar expression patterns ordered by Pearson correlation coefficient were AURKB (R = 0.69, P < 0.0001), CDC20 (R = 0.70, P < 0.0001), CCNB1 (R = 0.69, P < 0.0001), KIF2C (R = 0.66, P < 0.0001), and RAD54L (R = 0.64, P < 0.0001) (Fig. 4 A). Metascape was used to visualise and analyse the interactome network composed of the top 100 PTTG1 related genes. The PPI network has 90 nodes and 599 edges, indicating a wide range of interactions between these proteins (Fig. 4 B). The MCODE algorithm was then applied to this network to find sites where proteins are highly linked. Following that, each MCODE network underwent GO enrichment analysis. The results revealed that five MCODE complexes were discovered in the PPI network, with 'cell cycle' being the most common biological meaning (Fig. 4 C). To further study the functions of the top 100 PTTG1 similar genes, GO enrichment analysis and KEGG pathway analysis were used. These proteins have a significant role in various cellular processes, including nuclear division, organelle fission, microtubule binding, and tubulin binding (P < 0.0001) (Fig. 4 D). Pathway enrichment revealed that PTTG1-related genes function in multiple pathways, including cell cycle and DNA replication (P < 0.0001) (Fig. 4 D). These findings indicated that cancer-related genes and pathways were common in PTTG1 similar genes. Correlations between PTTG1 expression and immune cell infiltration in pan-cancer We investigated the relationship between immune cell infiltration and PTTG1 gene expression in 33 malignancies using GSCA. ImmuCellAI was performed to get the infiltrates of immune cells in each TCGA sample. In most types of cancer, PTTG1 correlated positively with T cell exhaustion, Th1, B cells, DC, effector memory T cells, Infiltration Score, CD8 naive T cells, CD8 T cells, cytotoxic T cells, and nTreg, but negatively with CD4_T, CD4_naive, central memory T cells, MAIT, neutrophil, NKT, and Th17 cells (Fig. 5 A, Supplementary table 1 ). The ESTIMATE package in R was used to determine the connection between ESTIMATEScore, ImmunoScore, and StromalScore and PTTG1 expression in TCGA pan-cancer datasets. PTTG1 expression showed a positive correlation with ImmunoScore, StromalScore, and ESTIMATEScore in GBMLGG and KIPAN (Pearson R > 0.2 and Pearson P value < 0.05). PTTG1 expression had a negative connection with ImmunoScore, StromalScore, and ESTIMATEScore in GBM, TGCT, READ, STES, and LUSC (Pearson R<-0.2 and Pearson P value < 0.05, Fig. 5 B, Supplementary table 2 ). The connection of PTTG1 with immune cell infiltration may help to explain the poor prognosis caused by its high expression. The expression level of PTTG1 is associated with response to immunotherapy treatment To investigate the possibility of PTTG1 as an immune checkpoint blockade response biomarker, the relationship between PTTG1 and immunotherapy response was examined using the TIDE database. The results showed that high expression of PTTG1 significantly affected the efficacy of immune checkpoint blockade (anti-PD1) and adoptive T cell therapy (ACT), reducing the OS of patients in the Braun2020_PD1_Kidney_Clear cohort, Lauss2017_ACT_Melanoma cohort, and Riaz017_PD1_Melanoma_lpi. Prog cohort (Fig. 6 A.B). To assess the accuracy of PTTG1 as an immunotherapy response biomarker, we examined it with other biomarkers previously associated with tumour immune evasion by TIDE. The area under the receiver operating characteristic curve (AUC) was used to assess the predictive ability of these biomarkers. PTTG1 produced an AUC greater than or equal to 0.5 in 11 of the 16 immune checkpoint blockade sub-cohorts (Fig. 6 C). PTTG1 outperformed previously published biomarkers in predicting immunotherapy prognosis for melanoma patients (Nathanson2017_CTLA4_Melanoma_Post cohort, AUC = 0.8636 and Gide2019_PD1 + CTLA4_Melanoma cohort, AUC = 0.7000) (Fig. 6 C). These findings revealed that PTTG1 played a role in antitumor immune response and promoted immunotherapy resistance. PTTG1 exacerbates CTL dysfunction and contributes to resistance against immunotherapy interventions The relationship between PTTG1 and Cytotoxic T lymphocyte (CTL) dysfunction was evaluated by TIDE. The results showed that a higher CTL level indicates better patient survival, but only when PTTG1 has a low expression level in glioma (OS, z = 2.81, P = 0.00501), myeloma (OS, z = 3.66, P = 0.00025), LUAD (OS, z = 2.25, P = 0.0241), LUSC (OS, z = 2.02, P = 0.0432), COAD (OS, z = 3.08, P = 0.0021), and KIRC (PFS, z = 2.75, P = 0.00604) (Fig. 7 A and Supplementary table 3). This finding suggests that a higher PTTG1 level in tumours reduces the positive connection between CTL and survival. Gene co-expression study revealed that the expression of PTTG1 was positively correlated with immune inhibitory marker such as EDNRB and C10orf54. And negatively correlated with immune stimulatory marker such as HMGB1, CD70, TNFSF9 and TNFRSF18 (Fig. 7 B). Tumour mutation burden (TMB) and homologous recombination deficit (HRD) are two clinically important immunological markers that are closely related to tumour immunotherapy. The link between PTTG1 expression and TMB or HRD scores was examined utilising the Sangerbox platform. We discovered a strong positive connection between PTTG1 mRNA expression and TMB or HRD scores in the majority of malignant tumours, particularly KICH (Fig. 7 C and D). These results suggested that PTTG1 was related to promoting tumor immune escape and immunotherapy resistance. PTTG1 mainly expressed in regulatory T cells and proliferative T cells To investigate the probable ways by which PTTG1 affects theted tumor immune microenvironment, public single-cell RNA-seq (scRNA) datasets were used to determine the expression level of PTTG1 on various immune cells. We discovered that PTTG1 was extensively expressed in immune cells, but it was particularly high in regulatory T cells (Treg) and proliferative T cells (Tprolif) in NSCLC, KIRC, and LIHC cohots (Fig. 8 A, B, C). Interestingly, immunotherapy responders' hepatic progenitor cells and malignant cells expressed PTTG1 more than non-responders in LIHC patients, indicating that PTTG1 was related with immune suppressive cells and immunotherapy resistance (Fig. 8 D). Cellular clusters and single-cell PTTG1 expression profile in different cellular types of NSCLC (A), KIRC (B), and LIHC (C). (D) Single-cell PTTG1 expression profile of non-responder and responder in LIHC patients with PD-L1/CTLA-4 treatment. Correlation of PTTG1 and drug response The CellMinerCDB was used to look into the relationship between PTTG1 expression and drug sensitivity (IC50) in the GDSC dataset. Pearson's correlation analysis demonstrated a negative connection between high PTTG1 expression and the -log10(IC50) values of 22 drugs (Fig. 9 A, Supplementary table 4), particularly MEK1/MEK2 inhibitors such as RDEA119, trametinib, CI-1040, selumetinib, and PD-0325901 (Fig. 9 B). These data showed that increasing PTTG1 could reduce drug sensitivity for these small compounds. PTTG1 showed a substantial positive correlation with the -log10(IC50) values of 13 drugs (Fig. 9 A, Supplementary table 4), including NSC-207895 (Fig. 9 C) and ABT-263 (Navitoclax) (Fig. 9 E). The results suggested that these 13 small compounds have the potential to be PTTG1 target medicines. A molecular docking model was developed to investigate the mechanism of interaction between PTTG1 and prospective target medicines. We discovered that NSC-207895 could bind to PTTG1 and remain in the binding pocket surrounded by critical residues (Lys1189, Pro1144, Leu1140, His1142, Cys1160, and Arg1197) (Fig. 9 D). ABT-263 could attach to PTTG1 and remain in the binding pocket surrounded by critical residues (Ala1671, Arg1675, Cys928, Glu925, Ala929, Leu945, Arg1638, Lys1645, Asp941, Gly931, Gln933, and Gln1674) (Fig. 9 F). Our findings suggested that PTTG1 could be a therapeutic target for MEK1/MEK2 inhibitor resistance. ABT-263 and NSC-207895 may be effective anticancer drugs that target PTTG1. Validation of PTTG1 expression and function in NSCLC We further confirmed the expression of PTTG1 in tumours by utilising the GEO database. The findings showed that NSCLC tissues had considerably greater levels of PTTG1 expression than did normal lung tissues (P < 0.01, Fig. 10 A). Additionally, we used the online Kaplan Meier plotter database to determine the predictive significance of PTTG1 in 1161 patients with LUAD. Poor patient survival was observed to be substantially correlated with high expression of PTTG1 (low vs. high: 110.27 vs. 60.73, P < 0.01) (Fig. 10 B). In the multi-drug resistance lung cancer dataset GSE77209, which included the parental cell lines H1299, H1355, and paclitaxel-carboplatin-resistant cells, we examined the expression of PTTG1. In H1299 and H1355, resistant cells expressed more PTTG1 than the parental cells did (Fig. 10 C). The expression of PTTG1 in chemoresistance cells was confirmed by qRT-PCR. The expression levels of PTTG1 in the paclitaxel-resistant cell lines A549-TXR and H358-TXR were much higher than those in the parental cells, A549 and H358 (Fig. 10 D). We employed short interfering RNAs to knock down PTTG1 in A549- TXR and H358- TXR in order to ascertain the biological roles of PTTG1 in paclitaxel-resistant cells (Fig. 10 D). The findings showed that PTTG1 knockdown reduced A549-TXR and H358-TXR's capacity to generate clones (Fig. 10 F). The transwell experiment verifies that PTTG1 knockdown prevents A549-TXR and H358-TXR cells from migrating (Fig. 10 G). These results imply that PTTG1 is involved in both chemoresistance and carcinogenesis. Discussion Human life is seriously threatened by cancer. It is useful to employ biomarkers that are often expressed in many tumour types as targets for treatment and as tumour biomarkers. PTTG1 is involved in the growth of tumours and may function as an oncogene in the development and growth of tumours 22 . We analyzed the role of PTTG1 in pan-cancer and confirmed that PTTG1 was overexpressed in 15 of 33 tumor types of TCGA datasets. The expression of PTTG1 was related to tumor classification in several cancers like ACC, LUAD, LIHC, BRCA, KIRC and KIRP. Moreover, high PTTG1 expression was related to poor patient survival, consistent with the previously reported role of PTTG1 in kidney renal clear cell carcinoma 23 and hepatocellular carcinoma 6 . An increasing number of research have investigated the relationship between genetic changes and cancer progression. We discovered that PTTG1 gene amplification resulted in significant protein expression in most tumours. Pathway enrichment revealed that PTTG1 comparable genes were involved in a variety of pathways, including cancer-related pathways such as 'cell cycle' and 'DNA replication'. These findings imply that PTTG1 has the potential to serve as a tumour diagnostic and prognostic marker. Furthermore, inhibiting aberrant PTTG1 expression at the genetic and protein levels could be a possible therapeutic method for reversing carcinogenesis. Tumor-produced chemicals and non-cancerous elements make up the tumour microenvironment. It is essential for the initiation, growth, metastasis, and reaction to treatment of tumours. One of the main mechanisms of tumour cell immune escape that leads to immunological dysfunction in cancer patients is T cell depletion 24 , 25 . Recent research has shown that T cell exhaustion is considered a mechanism of resistance for cellular immunotherapies 26 and a significant marker of poor outcomes in many cancers including breast cancer 27 and renal cell carcinoma 28 . Revitalization of exhausted T cells might improve immunity. In this context, recovered fatigued T cells may be a useful source of predictive biomarkers for a possible target. revitalising exhausted T cells might improve immunity. In this context, recovered fatigued T cells may be a useful source of predictive biomarkers for a possible target. In this study, we discovered that PTTG1 is positively connected with T cell exhaustion but negatively correlated with the ImmunoScore, StromalScore, and ESTIMATEScore in most types of tumours. Furthermore, PTTG1 was shown to be strongly expressed in immune-modulating cells such as Treg and Tprolif cells from NSCLC, KIRC, and LIHC patients. Treg cells produce an immuno-suppressive environment and are less sensitive to immune checkpoint inhibitors 29 , 30 . It was noteworthy that Tprolif cells were able to exhibit high expression levels of immunological exhaustion markers, including PDCD1, HAVCR2, CTLA4, LAG3, and TIGIT 31 . In both KIRC and LIHC, our results demonstrated a strong positive correlation between PTTG1 and the immune-suppressive genes HAVCR2, CTLA4, LAG3, TIGIT, and PDCD1. These findings imply that the immune-suppressive microenvironment and T cell exhaustion may be connected to the elevated expression of PTTG1. The immuno-suppressive tumour microenvironment makes immune checkpoint drugs significantly less effective than anticipated in the treatment of cancer 32 . To investigate PTTG1's involvement in immunotherapy resistance, the link between PTTG1 and immunotherapy response was examined. According to the results, patients with KIRC and melanoma undergoing immune checkpoint inhibition (anti-PD1) and adoptive T cell treatment (ACT) had a worse prognosis when their expression of PTTG1 was elevated. An important part of the antitumor immune system is played by cytotoxic T lymphocytes (CTLs). Immunotherapy resistance and tumour immune evasion are significantly aided by CTL malfunction. We discovered that higher CTL levels predicted longer patient life only when PTTG1 expression was low in glioma, myeloma, NSCLC, COAD, and KIRC. As a result, increased PTTG1 levels in tumours will reduce the positive correlation between CTL and survival. TMB and HRD are prognostic indicators for immune checkpoint inhibitor clinical effectiveness 33 . Particularly in KICH, we discovered a significant positive relationship between PTTG1 and TMB or HRD scores in most malignant tumours. These findings imply a relationship between tumour immunotherapy resistance and PTTG1 overexpression. The process might be connected to PTTG1's role in increasing cytotoxic T lymphocyte malfunction and mediating T cell exhaustion. It is possible to view PTTG1, which is expressed on Treg and Tprolif cells, as a novel therapeutic target to overcome immunotherapy resistance. A increasing amount of research indicates that PTTG1 plays a crucial role in chemosensitivity 34 . Pancreatic ductal adenocarcinoma patients with decreased PTTG1 have good treatment response with great sensitivity and selectivity 35 . In ovarian cancer cell lines, downregulating PTTG1 improved saracatinib sensitivity 36 . PTTG1 gene suppression reduces prostate cancer cell sensitivity to paclitaxel-induced apoptosis 37 . Consistent with earlier research, we found that PTTG1 was linked to chemosensitivity, particularly in MEK1/MEK2 inhibitors such as PD-0325901, RDEA119, trametinib, CI-1040, and selumetinib. ABT-263 and NSC-207895 may be employed as an efficient anticancer medication by targeting PTTG1, according to the molecular docking studies. Furthermore, we confirmed the expression and function of PTTG1 in NSCLC. We discovered that PTTG1 was considerably greater in NSCLC tissues compared to non-tumor lung tissues. Furthermore, elevated PTTG1 expression was found to be substantially associated with poor patient survival in NSCLC. PTTG1 expression levels in paclitaxel-resistant cell lines A549-PTX and H358-PTX were much higher than in the parental cells A549 and H358, which is consistent with recent studies in LUAD 38 . PTTG1 knockdown inhibited clone formation and migration ability of A549- TXR and H358- TXR cells. To summarise, our investigations indicate that PTTG1 may have potential as a tumour diagnostic, prognostic, and chemosensitivity marker. Reversing carcinogenesis and chemoresistance may be achieved by blocking aberrant PTTG1 expression at the genetic and protein levels. Increased PTTG1 expression is associated with resistance to tumour treatment. The process might be connected to PTTG1's role in increasing cytotoxic T lymphocyte malfunction and mediating T cell exhaustion. It is possible to view PTTG1, which is expressed on Treg and Tprolif cells, as a novel therapeutic target to overcome immunotherapy resistance. Abbreviations ACC Adrenocortical carcinoma BLCA Bladder Urothelial Carcinoma BRCA,Breast invasive carcinoma CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma CHOL Cholangio carcinoma COAD Colon adenocarcinoma DLBC Lymphoid Neoplasm Diffuse Large B-cell Lymphoma ESCA Esophageal carcinoma GBM Glioblastoma multiforme HNSC Head and Neck squamous cell carcinoma KICH Kidney Chromophobe KIRC Kidney renal clear cell carcinoma KIRP Kidney renal papillary cell carcinoma LAML Acute Myeloid Leukemia LGG Brain Lower Grade Glioma LIHC Liver hepatocellular carcinoma LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma MESO Mesothelioma OV Ovarian serous cystadenocarcinoma PAAD Pancreatic adenocarcinoma PCPG Pheochromocytoma and Paraganglioma PRAD Prostate adenocarcinoma READ Rectum adenocarcinoma SARC Sarcoma SKCM Skin Cutaneous Melanoma STAD Stomach adenocarcinoma TGCT Testicular Germ Cell Tumors THCA Thyroid carcinoma THYM Thymoma UCEC Uterine Corpus Endometrial Carcinoma UCS Uterine Carcinosarcoma UVM Uveal Melanoma. Declarations Author contributions Lihui Wang and Chunlin Zou designed the study. Handong Wei, Yashu Ma and Shuxing Chen collected the data. All Authors analyzed the data and wrote the manuscript. All authors contributed to the article and approved the submitted version. Funding This work was supported by the Joint Project on Regional High-Incidence Diseases Research of Guangxi Natural Science Foundation under Grant (No. 2023GXNSFAA026338), the National Natural Science Foundation of China (81803564), and the National College Students Innovation and Entrepreneurship Training Program (No. 202210598040；No. 202210598038). CONFLICT OF INTEREST The authors declare that there are no conflicts of interest. Ethics, Consent to Participate, and Consent to Publish declarations Not applicable. References Yanagida, M. (2005). Basic mechanism of eukaryotic chromosome segregation. Philos Trans R Soc Lond B Biol Sci 360 , 609-621. 10.1098/rstb.2004.1615. 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Supplementary Files PTTG1SupplymentFigure1.tif Supplymenttable.xlsx Cite Share Download PDF Status: Published Journal Publication published 25 Oct, 2024 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 19 Aug, 2024 Editor assigned by journal 19 Aug, 2024 Submission checks completed at journal 19 Aug, 2024 First submitted to journal 16 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4923978","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342067953,"identity":"e73d4351-78e1-46cf-bb43-c93d13d76fbb","order_by":0,"name":"Handong Wei","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Handong","middleName":"","lastName":"Wei","suffix":""},{"id":342067955,"identity":"d103d57f-9044-402b-ab80-40b2c3163429","order_by":1,"name":"Yaxin Ma","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yaxin","middleName":"","lastName":"Ma","suffix":""},{"id":342067956,"identity":"bde8d3e8-db6a-41c1-9ddb-5fd0b83e7a10","order_by":2,"name":"Shuxing Chen","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuxing","middleName":"","lastName":"Chen","suffix":""},{"id":342067957,"identity":"7cdb0eb7-e205-4607-b3a4-793109b8c534","order_by":3,"name":"Chunlin Zou","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunlin","middleName":"","lastName":"Zou","suffix":""},{"id":342067958,"identity":"0752cfc2-14a0-4a11-abbd-bbff078cd92d","order_by":4,"name":"Lihui Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYBACAwbmxgNgFjtjG5BrkwDmJBTg08LYANHCDNaSlsDABtJiQJQWsNrDEC0MeLSYszc2HPjYZpcn78zc9pin4Hwev3x34ocHBgzy/GIHsGqx7DnYcHBmW3Kx4WHGdmMeg9vFkm28myWADjOcOTsBu8NuJDYc5t3GnLixmbFNGqglccMx3g0gLQkGt3Fouf8QpKUepuUcSMvmH3i13GAEaTmcOJ8ZrOUASMs2/LacSQT65d/xxA1ALZJzDJITZ7blbrNIMJDA7Zfjhw8++HCmOnF+e/sziTd/7BL7mc9uvvmjwkaeXxq7FoTeA6h8CfzKQUC+gbCaUTAKRsEoGKEAAACLZZvrsW9fAAAAAElFTkSuQmCC","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lihui","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-08-16 09:21:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4923978/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4923978/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-024-13060-5","type":"published","date":"2024-10-25T15:58:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64599012,"identity":"72cb74c6-e698-40d9-be57-0a16651af970","added_by":"auto","created_at":"2024-09-16 11:33:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3682825,"visible":true,"origin":"","legend":"\u003cp\u003eThe mRNA and protein expression of PTTG1 in pan-cancer.\u003c/p\u003e\n\u003cp\u003e(A) The mRNA expression level of PTTG1 in tumor tissues and adjacent normal tissues in 33 TCGA dataset. The red or purple plots represent the tumor sample and blue plots represent normal sample, respectively. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. (B) The mRNA expression level of PTTG1 in tumor and normal tissues from TCGA and GTEx data sets. The red letters represent significantly high expression in tumor and green letters represent low expression, respectively. (C) Pan-cancer analysis of PTTG1 gene expression at different tumor stages. (D) Immunohistochemical staining images of PTTG1 in human cancers and normal tissue from HPA database.\u003c/p\u003e","description":"","filename":"PTTG1figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/651bc037dd20c4f9250e6d91.png"},{"id":64598133,"identity":"da0cdf1e-91cf-4115-bf39-4c92cd0f74a0","added_by":"auto","created_at":"2024-09-16 11:17:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":542738,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival map of PTTG1 in pan-cancer. GEPIA2 survival analysis including OS.\u003c/p\u003e\n\u003cp\u003e(A) and DFS (B) demonstrated the survival map (upper panel) and Kaplan–Meier survival plots (lower panel) of PTTG1 in TCGA dataset.\u003c/p\u003e","description":"","filename":"PTTG1figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/1e68a4eae70fe629a2178bfa.png"},{"id":64598135,"identity":"c9d966a9-aba7-4a1e-8c92-afe311fe383b","added_by":"auto","created_at":"2024-09-16 11:17:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1204969,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic alteration analysis of PTTG1 in various TCGA tumors.\u003c/p\u003e\n\u003cp\u003e(A) The profile of SNV (Single Nucleotide Variation) of PTTG1 in the TCGA cohorts. (B) The lollipop plot presents the mutation site, type, and count of PTTG1 in the sample set of TCGA UCEC and STAD cohorts. (C) The SNV classes of PTTG1 in the TCGA cohorts. (Variant Classification): the count of each type of deleterious mutation; (Variant Type): the count of SNP and DEL of PTTG1; (SNV class): the count of each SNV class of PTTG1. (D) Pie plot summarizes the CNV (Copy Number Variation) of PTTG1 in the TCGA datasets. (E) The difference of survival between PTTG1 CNV and wide type in the TCGA datasets. (F) Overall survival (OS), progression free survival (PFS), disease free interval (DFI) and disease specific survival (DSS) of PTTG1 CNV and wide type in the TCGA KIRP cohort.\u003c/p\u003e","description":"","filename":"PTTG1figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/1bbd3ca55ceb3a186c347357.png"},{"id":64598142,"identity":"72eb69ca-7524-4c16-aa7b-206d309de7d7","added_by":"auto","created_at":"2024-09-16 11:17:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1267918,"visible":true,"origin":"","legend":"\u003cp\u003ePPI, GO and KEGG analysis of PTTG1 and similar genes.\u003c/p\u003e\n\u003cp\u003e(A) Correlation analysis of PTTG1 and top 5 similar genes in TCGA database. (B) PPI network of PTTG1 and top 100 similar genes, where the MCODE complexes are colored according to their identities. (C)The five MCODE complexes identified in PPI network by Metascape, colored by their identities. The top-three functional enriched terms of each MOCDE network were listed at the right bottom. (D) Cellular component, molecular function, biological process, and KEGG analysis of PTTG1 and similar genes.\u003c/p\u003e","description":"","filename":"PTTG1figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/285c78dd7d812e62a2a40fdc.png"},{"id":64599013,"identity":"6edbf426-aacb-4f10-ac04-bae4e4f7a1ee","added_by":"auto","created_at":"2024-09-16 11:33:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1367430,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between PTTG1 expression and immune infiltration.\u003c/p\u003e\n\u003cp\u003e(A) Heatmap summarizes the significance of P value and FDR for the spearman correlation analysis between PTTG1 expression and immune cells' infiltrates (*: \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05; #: FDR≤0.05). (B) The correlation between PTTG1 expression and ImmunoScore, StromalScore or ESTIMATE Score.\u003c/p\u003e","description":"","filename":"PTTG1figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/197f8da2f0ac805738b9f6ad.png"},{"id":64598650,"identity":"72aa26d9-0b41-4a93-99a4-4591d3dc704c","added_by":"auto","created_at":"2024-09-16 11:25:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":885730,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between PTTG1 expression and immunotherapy response.\u003c/p\u003e\n\u003cp\u003e(A) The plot showed the prognostic value of PTTG1 versus published biomarkers in melanoma and kidney renal clear cell carcinoma cohort. The x-axis shows the z-score on Cox-PH regression and the y-axis indicates its significance level (two-sided Wald test) (B) The association of PTTG1 with patients' overall survival through Kaplan-Meier curves in melanoma and kidney renal clear cell carcinoma patients with immunotherapy treatment. (C) The bar chart showed the correlation between PTTG1 and published biomarkers in the immunotherapy cohorts. The AUC was used to evaluate the predictive performance of the test biomarker on the immunotherapy response state.\u003c/p\u003e","description":"","filename":"PTTG1figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/01971a11ebecdeb58f3f09e4.png"},{"id":64598653,"identity":"0efc1a08-777e-46fc-ad14-659ed11e90e1","added_by":"auto","created_at":"2024-09-16 11:25:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1747357,"visible":true,"origin":"","legend":"\u003cp\u003eMechanisms of PTTG1 promoting immunotherapy resistance.\u003c/p\u003e\n\u003cp\u003e(A) The relationship between PTTG1 and CTL dysfunction. (B) Association of PTTG1 with immune checkpoint genes in pan-cancer. (C) The correlations between PTTG1 expression and TMB scores with Pearson correlation. (D) The correlations between PTTG1 expression and HRD scores with Pearson correlation. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"PTTG1figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/7996ce2db6475ca90adabe56.png"},{"id":64598138,"identity":"a826351e-7163-41ab-a250-daf5035e7773","added_by":"auto","created_at":"2024-09-16 11:17:14","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":928624,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell data analysis of PTTG1 expression by TISCH2.\u003c/p\u003e\n\u003cp\u003eCellular clusters and single-cell PTTG1 expression profile in different cellular types of NSCLC (A), KIRC (B), and LIHC (C). (D) Single-cell PTTG1 expression profile of non-responder and responder in LIHC patients with PD-L1/CTLA-4 treatment.\u003c/p\u003e","description":"","filename":"PTTG1figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/645e0e37b0f3257276e45fa6.png"},{"id":64598141,"identity":"4cdb820b-8b40-47b0-a815-bbffe092b5ca","added_by":"auto","created_at":"2024-09-16 11:17:14","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1849355,"visible":true,"origin":"","legend":"\u003cp\u003ePTTG1 increased the drug resistance of cancer cells.\u003c/p\u003e\n\u003cp\u003e(A) The correlation between PTTG1 expression and the sensitivity [-log10(IC50)] of GDSC drugs. A depiction of the correlation between PTTG1 expression and MEK1/MEK2 inhibitor. (B) NSC-207895 (C) and ABT-263 (E) response obtained from the GDSC panel. (D) The predicted interaction of NSC-207895 with PTTG1 protein. Left: NSC-207895 binding with the pocket of PTTG1 is composed of hydrogen bonds. Right: the 2D hydrogen bond interaction pattern of NSC-207895 upon binding to PTTG1 protein. (E) The predicted interaction of ABT-263 with PTTG1 protein. Left: ABT-263 binding with the pocket of PTTG1 is composed of hydrogen bonds. Right: the 2D hydrogen bond interaction pattern of ABT-263 upon binding to PTTG1 protein.\u003c/p\u003e","description":"","filename":"PTTG1figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/5f082d29fd27c5f2c933f76d.png"},{"id":64599014,"identity":"8081c68c-655b-4055-bc8a-2d573a8372b7","added_by":"auto","created_at":"2024-09-16 11:33:14","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":965617,"visible":true,"origin":"","legend":"\u003cp\u003eThe validation of PTTG1 characterization in lung adenocarcinoma.\u003c/p\u003e\n\u003cp\u003e(A) The expression of PTTG1 was analyzed using the Wilcoxon signed-rank test in GSE32863, GSE19188 and GSE31210 NSCLC datasets. (B) Kaplan–Meier curves and log-rank test of PTTG1 in NSCLC data sets. (C) The mRNA expression of PTTG1 in H1299, H1355 parental and resistance cells in GSE77209 dataset. T[n]: Resistant cells generated after ‘n’ cycles of paclitaxel-carboplatin treatment. (D) The mRNA expression levels of PTTG1 in parental cells A549, H358 and paclitaxel resistant cells A549- TXR, H358- TXR were detected by RT‑qPCR. (E) PTTG1 siRNA knockdown efficiency in A549- TXR and H358- TXR measured. (F) Clone formation assays with A549- TXR and H358- TXR cells transfected with PTTG1 siRNA. (G) Transwell assays with A549- TXR and H358- TXR cells transfected with PTTG1 siRNA. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"PTTG1figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/eedd7167fe4cf349c00696a9.png"},{"id":67683999,"identity":"26896edd-cb00-48ce-a5b2-f04ed678e4e7","added_by":"auto","created_at":"2024-10-28 16:22:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12237791,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/888b55f6-2363-420b-aaf7-bf5cd020e7fa.pdf"},{"id":64598144,"identity":"6a272d55-80c0-4671-a7d0-11be8a233a2d","added_by":"auto","created_at":"2024-09-16 11:17:15","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11771480,"visible":true,"origin":"","legend":"","description":"","filename":"PTTG1SupplymentFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/c8e8eb713a62feaa9613c6be.tif"},{"id":64598145,"identity":"6ea6be97-19cc-4250-b6c2-829b41adcc0b","added_by":"auto","created_at":"2024-09-16 11:17:15","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":70386,"visible":true,"origin":"","legend":"","description":"","filename":"Supplymenttable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4923978/v1/a39e208d4e48c4ca3e7a4019.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics analysis Identifies PTTG1 as a prognostic biomarker associated with immunotherapy and chemotherapy resistance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe pituitary tumor-transforming gene 1 (PTTG1), also known as SECURIN was isolated from pituitary tumors and identified as a gene with tumorigenic effect in vivo. It encodes a homolog of the yeast Securin protein and acts as a sister chromatid regulator that inhibits separase function and thus prevents sister chromatid separation.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. As a transcriptional activator, PTTG1 can activate C-Myc and up-regulate the expression of cyclinB1 and CDK1 \u003csup\u003e3\u003c/sup\u003e. PTTG1 plays a role in chromosomal stability maintenance, orderly cell cycle monitoring, genetic stability and mitotic fidelity assurance, malignant transformation, tumour induction, invasion, and metastasis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eResearch studies indicate that overexpression of PTTG1 is associated with unfavorable tumor phenotype and adverse prognosis. This shows that PTTG1 plays a critical role in the occurrence and progression of tumours. For instance, hepatocellular carcinoma has an elevated overexpression of PTTG1, which enhances the synthesis of asparagine to activate mTOR and advance the tumour\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Furthermore, it was shown that a higher level of PTTG1 was associated with poorer prognosis for LUAD patients\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and targeted PTTG1 inhibition improves radiation-induced antitumour immunity in lung adenocarcinoma\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Additionally, the predictive values of PTTG1 for immunotherapy response\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and chemosensitivity \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e were also revealed. Although the importance of PTTG1 in tumors is becoming more widely known, there have been no comprehensive studies of its function in tumor occurrence, recurrence, immunotherapy or chemotherapy.\u003c/p\u003e \u003cp\u003eIn this work, we evaluated the gene expression, prognostic value, and relate to immunotherapy response of PTTG1 in many well-established databases. We discovered that PTTG1 is overexpressed in several tumours and is related with poor survival in a variety of malignancies. PTTG1 promotes cytotoxic T cell malfunction, which contributes to immunotherapy resistance. PTTG1 was found to be significantly related with sensitivity to several anticancer medicines, particularly MET inhibitors. The current findings highlighted the critical function of PTTG1 in carcinogenesis, immunotherapy, and chemotherapy response, identifying a new target for cancer treatment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGene and protein expression analysis\u003c/h2\u003e \u003cp\u003eThe differential expression of PTTG1 in tumor and neighboring normal tissues across all TCGA tumors was analyzed by TIMER (Tumour Immune Estimation Resource, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e10\u003c/sup\u003e. The Wilcoxon test was used to determine the statistical significance of differential expression. The PTTG1 gene expression profile and pathological stage plot across all tumor samples and paired normal tissues were analyzed by GEPIA2 (Gene Expression Profiling Interactive Analysis 2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/#index\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/#index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e11\u003c/sup\u003e. The immunohistochemistry-based protein expression profiles of PTTG1 were analyzed by the Human Protein Atlas (hpa, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eKaplan\u0026ndash;Meier Test\u003c/h2\u003e \u003cp\u003eSurvival analysis of PTTG1 in tumor patients was obtained from the GEPIA2 based on the TCGA datasets. Kaplan-Meier Plotter \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com/analysis/\u003c/span\u003e\u003cspan address=\"http://kmplot.com/analysis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to validate the prognostic value of PTTG1 in NSCLC patients. The Kaplan-Meier method was performed to plot the overall survival (OS) or disease-free survival (DFS, also called relapse-free survival and RFS) curves. Survival differences were assessed by the log-rank test using the median of PTTG1 as a cutoff value. The cox proportional hazard ratio (HR) with 95% confidence intervals and log‑rank \u003cem\u003eP\u003c/em\u003e‑values were calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenetic alteration analysis\u003c/h2\u003e \u003cp\u003eThe PTTG1 alteration landscape in human cancers was depicted based on TCGA pan-cancer atlas studies by cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. To determine the correlation with copy number variation and survival, patients were divided into delete, amplification, and wild-type groups based on PTTG1 mutation type. The Kaplan-Meier method was performed to plot the overall survival curves in TCGA datasets by GSCA (Gene Set Cancer Analysis, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guolab.wchscu.cn/GSCA/#/\u003c/span\u003e\u003cspan address=\"https://guolab.wchscu.cn/GSCA/#/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eSimilar Genes Detection and PPI network construction\u003c/h2\u003e \u003cp\u003eTo identify genes with a similar expression pattern to PTTG1 in pan-cancer, the Pearson correlation coefficients were analyzed using the GEPIA2 database. The similar genes were analyzed using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to generate the PPI network. PPI pairs with a combined score\u0026thinsp;\u0026gt;\u0026thinsp;0.4 were extracted. The PPI network was visualized using Cytoscape 3.7.2 software \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and the most important module was performed using the MCODE plug-in in Cytoscape software. The Metascape web-based tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/gp/index.html\u003c/span\u003e\u003cspan address=\"https://metascape.org/gp/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e was used for functional enrichment analysis (GO analysis and KEGG pathway analysis) of these genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003ePTTG1 and tumor immune microenvironment\u003c/h2\u003e \u003cp\u003eThe correlation of PTTG1 expression with immune infiltration level in diverse cancer types was analyzed by TIMER (Tumor IMmune Estimation Resource, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e10\u003c/sup\u003e. The TCGA pan-cancer data set was retrieved from the UCSC database. The PTTG1 expression data in individual samples was retrieved, and a log2(x\u0026thinsp;+\u0026thinsp;1) transformation was applied. The immune, stromal, and ESTIMATE scores for each sample were determined using the R package ESTIMATE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.mdanderson.org/estimate/rpackage.html\u003c/span\u003e\u003cspan address=\"https://bioinformatics.mdanderson.org/estimate/rpackage.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pearson correlation analysis was used to identify significantly linked immune infiltration scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003ePTTG1 and immunotherapy response\u003c/h2\u003e \u003cp\u003eTIDE (Tumor Immune Dysfunction and Exclusion, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a computational framework created to assess the potential of tumour immune escape from the gene expression profiles of cancer samples\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The 'Biomarker Evaluation' module was used to compare PTTG1 to other reported biomarkers in terms of their predictive ability for response outcome and overall survival. The 'Query Gene' module calculated the PTTG1 gene signature in T-cell dysfunction using data from the TCGA, PRECOG, and METABRIC databases. The T cell dysfunction score of PTTG1 is defined as the Wald test z score \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The two-sided Wald test was used to calculate the relationship between cytotoxic T lymphocyte CTL levels and overall survival. The Kaplan-Meier plot divides tumours into two groups: 'High CTL' has above-average CTL values across all samples, and 'Low CTL' has values below average. To demonstrate the relationship between CTL levels and survival outcomes, samples were divided based on their PTTG1 expression levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eExpression level of PTTG1 at the single‑cell level\u003c/h2\u003e \u003cp\u003eTISCH2 (Tumor Immune Single-cell Hub 2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.comp-genomics.org/\u003c/span\u003e\u003cspan address=\"http://tisch.comp-genomics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a scRNA-seq database that focusses on the tumour microenvironment. It provides precise cell-type annotation at the single-cell level, allowing the analysis of TME across various cancer types\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The expression level of PTTG1 at the single-cell level was visualized by TISCH2 in the NSCLC_GSE139555, KIRC_GSE111360, and LIHC_GSE140228 datasets. The PTTG1 expression between immunotherapy responders and non-responders was investigated in the LIHC_GSE125449_aPDL1aCTLA4 dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003ePTTG1 and chemo response\u003c/h2\u003e \u003cp\u003eCellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb)\u003csup\u003e20\u003c/sup\u003e enables the analysis of molecular and pharmacological data across cancer cell line databases to identify medications that match genomic determinants of response. CellMinerCDB investigated the relationship between PTTG1 expression and chemotherapy response using the GDSC (Sanger/Massachusetts General Hospital Genomics of Drug Sensitivity in Cancer) dataset.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMoleular Docking\u003c/h2\u003e \u003cp\u003eThe X-ray crystal structure of PTTG1 (7nj0) was obtained from the RCSB PDB protein databank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The 3D formats of ABT-263 (ZINC150338726) and NSC-207895 (ZINC5180959) were obtained from ZINC15 drug database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://zinc15.docking.org/\u003c/span\u003e\u003cspan address=\"http://zinc15.docking.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e21\u003c/sup\u003e. Discovery Studio 4.5 Client deleted the water and ligand molecules from the crystal structures before starting the docking simulation. PyRx software was used to simulate drug docking and PTTG1 interactions. We calculated interaction energies to predict docking positions and pick the binding pose with the lowest binding energy (kcal mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The results were visualised and analysed with Discovery Studio 4.5 Client.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell lines\u003c/h2\u003e \u003cp\u003eHuman lung cancer cell lines A549, H358 and the paclitaxel resistant cell lines A549-TXR, H358-TXR were kindly provided by Professor Wang Luo (University of Michigan, USA). Cells were maintained in RPMI 1640 or F12K supplemented with 10% FBS and 1% antibiotic-antimycotic. All cell lines were cultured at 37\u0026deg;C in a 5% CO2 cell culture incubator. Mycoplasma contamination was excluded in these cell lines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReal-Time Quantitative PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was reverse transcribed into cDNA using Reverse Transcription Kit (Thermo Fisher Scientific). qRT-PCR was performed using Power SYBR\u0026trade; Green (Thermo Fisher Scientific) and ABI 7300 detection system (Applied Biosystems). qRT-PCR data were normalized to the expression of housekeeping gene GAPDH, and relative expressions were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. The oligonucleotide primers were shown as follows: PTTG1 (forward: 5\u0026rsquo;- ATGAATGCGGCTGTTAAGACCTG-3\u0026rsquo;, reverse: 5\u0026rsquo;- TCCCATCTAAGGCTTTGATTGAAGG-3\u0026rsquo;). All tests were performed in triplicate, and the data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003esiRNA-mediated knockdown\u003c/h2\u003e \u003cp\u003eCells were seeded at the desired concentration in 60 mm plates and then transfected with 10 nM experimental siRNA oligonucleotides or non-targeting controls 24 hours later. Lipofectamine\u0026reg; RNAiMax Reagent (Invitrogen, USA) was used to knockdown cells in OptiMEM medium, following the manufacturer's recommendations. The knockdown efficiency was measured using qPCR. PTTG1 siRNA sequences for knockdown tests are listed below: sense: 5'-UGUGGUUGCUAAGGAUGGGCUTT-3'; antisense: 5'- AGCCCAUCCUUAGCAACCACATT-3'.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClone Formation Assay\u003c/h2\u003e \u003cp\u003eA549-TXR and H358-TXR cells (250 per well) were planted in 12-well plates and grown overnight. Cells were transfected with 10 nM experimental siRNA oligonucleotides or non-targeting controls 24 hours after plating. After two weeks, the cells were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Each well's clone count was recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBasement membrane migration assays\u003c/h2\u003e \u003cp\u003eCells were treated with the siRNAs listed above to perform invasion tests. After 48 hours of transfection, cells were trypsinised, and diluted to the desired concentration. Cells were seeded into basement membrane matrix Boyden chambers (8-mm pore size, BD) located in the insert of a 24-well culture plate. The lower compartment received 20% FBS as a chemoattractant. After 48 hours, the non-migrating cells were gently removed using a cotton swab. Cells on the chamber's lower side were stained with Diff-QuikTM Stain Set (SIEMENS), then air dried and photographed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eANOVA was used to compare PTTG1 expression levels in tumour and normal samples from the TCGA and GTEx datasets. Pearson's correlation coefficient was used to analyse the correlation between Immuno Score, Stromal Score, and ESTIMATE Score, as well as immunological checkpoints. The \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was judged significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003ePTTG1 is overexpressed in pan cancer and associated with tumor stages\u003c/h2\u003e\n \u003cp\u003eWe performed a gene expression examination for PTTG1 on the gene expression matrix derived from the TCGA datasets using TIMER. PTTG1 mRNA expression was considerably higher in BLCA, BRCA, CHOL, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, STAD, and UCEC, but significantly lower in THCA. The remaining cancer types exhibited no significant variation in PTTG1 mRNA levels between normal and tumor cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). The PTTG1 mRNA expression in pan-cancer was then analyzed using RNA sequencing datasets from the TCGA and GTEx projects by GEPIA. In addition to the 14 cancer types listed above, PTTG1 was significantly overexpressed in ACC, CESC, DLBC, GBM, OV, PAAD, SKCM, THYM, and UCS while being underexpressed in LAML and TGCT (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). To validate the diagnostic and prognostic relevance of PTTG1 in pan cancer, we used GEPIA to investigate its expression in tumor pathological stages. The findings revealed that PTTG1 was overexpressed in samples from advanced cancer patients with KIRC, KIRP, ACC, LUAD, LIHC, and BRCA (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). We then obtained the immunohistochemistry staining of PTTG1 from the HPA database. The PTTG1 protein stained darker in LUAD, BRCA, LIHC, and KIRC samples (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). The findings indicate that the PTTG1 protein may be significantly expressed in these tumors.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003ePTTG1 is a predictor of poor patient survival\u003c/h2\u003e\n \u003cp\u003eTo investigate the prognostic benefit of PTTG1, we compared survival (OS and DFS) differences between high and low PTTG1 expression groups in pan-cancer. The high PTTG1 expression group had substantially lower overall survival (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the low expression group in ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, THCA, and UVM (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Increased PTTG1 expression was linked to decreased disease-free survival in ACC, KIRC, KIRP, LGG, LIHC, MESO, PAAD, PRAD, SARC, and UVM (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). These findings indicate that PTTG1 is overexpressed and linked with poor outcomes in ACC, KIRC, KIRP, LIHC, LUAD, and PAAD, and has the potential to be a predictive biomarker.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociations of PTTG1 expression with genomic alterations in pan-cancer\u003c/h2\u003e\n \u003cp\u003ecBioPortal was used to analyse genetic variations (mutation, structural variant, amplification, and deep deletion) in PTTG1 across malignancies. In the majority of cancer types, PTTG1 gene amplification was obvious. High amplification was observed in KIRC, CHOL, and UCS (Supplementary Figs. 1A, B). The GSCA database was then used to analyse the frequency of change in single nucleotide variation (SNV) and copy number variation (CNV) in the PTTG1 gene. The findings revealed that SNV of PTTG1 is more frequently found in UCEC (0.88%, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB) and STAD (0.68%, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB), with low frequency in the other cancer types (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). The most common detrimental mutation (Missense_Mutation, Splice_Site, Frame_Shift_Del, etc.) in the PTTG1 gene is missense mutation. Point mutation analysis revealed that the SNV of PTTG1 had C\u0026thinsp;\u0026gt;\u0026thinsp;T and C\u0026thinsp;\u0026gt;\u0026thinsp;A transversions (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Next, we looked for CNV of the PTTG1 gene across malignancies. The findings revealed that CNV of PTTG1 was common in KIRC, ACC, CHOL, BLCA, TGCT, and LUSC, but uncommon in THCA, LAML, and PRAD (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). We investigated at the Kaplan-Meier survival curves for PTTG1 CNV (homo amplification and homo deletion) and broad type (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE). KIRP patients with homo deletion and homo amplification of PTTG1 genes exhibited a poor prognosis compared to the wild type group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eProtein-protein interactions of PTTG1 and similar genes in pan-cancer\u003c/h2\u003e\n \u003cp\u003eIn order to investigate the probable mechanism of PTTG1 in carcinogenesis, we identified the top 100 genes with similar expression patterns to PTTG1 in the TCGA cancer cohort using GEPIA2. The top five genes with similar expression patterns ordered by Pearson correlation coefficient were AURKB (R\u0026thinsp;=\u0026thinsp;0.69, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), CDC20 (R\u0026thinsp;=\u0026thinsp;0.70, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), CCNB1 (R\u0026thinsp;=\u0026thinsp;0.69, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), KIF2C (R\u0026thinsp;=\u0026thinsp;0.66, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and RAD54L (R\u0026thinsp;=\u0026thinsp;0.64, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Metascape was used to visualise and analyse the interactome network composed of the top 100 PTTG1 related genes. The PPI network has 90 nodes and 599 edges, indicating a wide range of interactions between these proteins (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). The MCODE algorithm was then applied to this network to find sites where proteins are highly linked. Following that, each MCODE network underwent GO enrichment analysis. The results revealed that five MCODE complexes were discovered in the PPI network, with \u0026apos;cell cycle\u0026apos; being the most common biological meaning (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). To further study the functions of the top 100 PTTG1 similar genes, GO enrichment analysis and KEGG pathway analysis were used. These proteins have a significant role in various cellular processes, including nuclear division, organelle fission, microtubule binding, and tubulin binding (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). Pathway enrichment revealed that PTTG1-related genes function in multiple pathways, including cell cycle and DNA replication (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). These findings indicated that cancer-related genes and pathways were common in PTTG1 similar genes.\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eCorrelations between PTTG1 expression and immune cell infiltration in pan-cancer\u003c/h2\u003e\n \u003cp\u003eWe investigated the relationship between immune cell infiltration and PTTG1 gene expression in 33 malignancies using GSCA. ImmuCellAI was performed to get the infiltrates of immune cells in each TCGA sample. In most types of cancer, PTTG1 correlated positively with T cell exhaustion, Th1, B cells, DC, effector memory T cells, Infiltration Score, CD8 naive T cells, CD8 T cells, cytotoxic T cells, and nTreg, but negatively with CD4_T, CD4_naive, central memory T cells, MAIT, neutrophil, NKT, and Th17 cells (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, Supplementary table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The ESTIMATE package in R was used to determine the connection between ESTIMATEScore, ImmunoScore, and StromalScore and PTTG1 expression in TCGA pan-cancer datasets. PTTG1 expression showed a positive correlation with ImmunoScore, StromalScore, and ESTIMATEScore in GBMLGG and KIPAN (Pearson R\u0026thinsp;\u0026gt;\u0026thinsp;0.2 and Pearson P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). PTTG1 expression had a negative connection with ImmunoScore, StromalScore, and ESTIMATEScore in GBM, TGCT, READ, STES, and LUSC (Pearson R\u0026lt;-0.2 and Pearson P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB, Supplementary table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The connection of PTTG1 with immune cell infiltration may help to explain the poor prognosis caused by its high expression.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eThe expression level of PTTG1 is associated with response to immunotherapy treatment\u003c/h2\u003e\n \u003cp\u003eTo investigate the possibility of PTTG1 as an immune checkpoint blockade response biomarker, the relationship between PTTG1 and immunotherapy response was examined using the TIDE database. The results showed that high expression of PTTG1 significantly affected the efficacy of immune checkpoint blockade (anti-PD1) and adoptive T cell therapy (ACT), reducing the OS of patients in the Braun2020_PD1_Kidney_Clear cohort, Lauss2017_ACT_Melanoma cohort, and Riaz017_PD1_Melanoma_lpi. Prog cohort (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA.B). To assess the accuracy of PTTG1 as an immunotherapy response biomarker, we examined it with other biomarkers previously associated with tumour immune evasion by TIDE. The area under the receiver operating characteristic curve (AUC) was used to assess the predictive ability of these biomarkers. PTTG1 produced an AUC greater than or equal to 0.5 in 11 of the 16 immune checkpoint blockade sub-cohorts (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). PTTG1 outperformed previously published biomarkers in predicting immunotherapy prognosis for melanoma patients (Nathanson2017_CTLA4_Melanoma_Post cohort, AUC\u0026thinsp;=\u0026thinsp;0.8636 and Gide2019_PD1\u0026thinsp;+\u0026thinsp;CTLA4_Melanoma cohort, AUC\u0026thinsp;=\u0026thinsp;0.7000) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). These findings revealed that PTTG1 played a role in antitumor immune response and promoted immunotherapy resistance.\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003ePTTG1 exacerbates CTL dysfunction and contributes to resistance against immunotherapy interventions\u003c/h2\u003e\n \u003cp\u003eThe relationship between PTTG1 and Cytotoxic T lymphocyte (CTL) dysfunction was evaluated by TIDE. The results showed that a higher CTL level indicates better patient survival, but only when PTTG1 has a low expression level in glioma (OS, z\u0026thinsp;=\u0026thinsp;2.81, P\u0026thinsp;=\u0026thinsp;0.00501), myeloma (OS, z\u0026thinsp;=\u0026thinsp;3.66, P\u0026thinsp;=\u0026thinsp;0.00025), LUAD (OS, z\u0026thinsp;=\u0026thinsp;2.25, P\u0026thinsp;=\u0026thinsp;0.0241), LUSC (OS, z\u0026thinsp;=\u0026thinsp;2.02, P\u0026thinsp;=\u0026thinsp;0.0432), COAD (OS, z\u0026thinsp;=\u0026thinsp;3.08, P\u0026thinsp;=\u0026thinsp;0.0021), and KIRC (PFS, z\u0026thinsp;=\u0026thinsp;2.75, P\u0026thinsp;=\u0026thinsp;0.00604) (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA and Supplementary table 3). This finding suggests that a higher PTTG1 level in tumours reduces the positive connection between CTL and survival. Gene co-expression study revealed that the expression of PTTG1 was positively correlated with immune inhibitory marker such as EDNRB and C10orf54. And negatively correlated with immune stimulatory marker such as HMGB1, CD70, TNFSF9 and TNFRSF18 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). Tumour mutation burden (TMB) and homologous recombination deficit (HRD) are two clinically important immunological markers that are closely related to tumour immunotherapy. The link between PTTG1 expression and TMB or HRD scores was examined utilising the Sangerbox platform. We discovered a strong positive connection between PTTG1 mRNA expression and TMB or HRD scores in the majority of malignant tumours, particularly KICH (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC and D). These results suggested that PTTG1 was related to promoting tumor immune escape and immunotherapy resistance.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003ePTTG1 mainly expressed in regulatory T cells and proliferative T cells\u003c/h2\u003e\n \u003cp\u003eTo investigate the probable ways by which PTTG1 affects theted tumor immune microenvironment, public single-cell RNA-seq (scRNA) datasets were used to determine the expression level of PTTG1 on various immune cells. We discovered that PTTG1 was extensively expressed in immune cells, but it was particularly high in regulatory T cells (Treg) and proliferative T cells (Tprolif) in NSCLC, KIRC, and LIHC cohots (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA, B, C). Interestingly, immunotherapy responders\u0026apos; hepatic progenitor cells and malignant cells expressed PTTG1 more than non-responders in LIHC patients, indicating that PTTG1 was related with immune suppressive cells and immunotherapy resistance (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eCellular clusters and single-cell PTTG1 expression profile in different cellular types of NSCLC (A), KIRC (B), and LIHC (C). (D) Single-cell PTTG1 expression profile of non-responder and responder in LIHC patients with PD-L1/CTLA-4 treatment.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n \u003ch2\u003eCorrelation of PTTG1 and drug response\u003c/h2\u003e\n \u003cp\u003eThe CellMinerCDB was used to look into the relationship between PTTG1 expression and drug sensitivity (IC50) in the GDSC dataset. Pearson\u0026apos;s correlation analysis demonstrated a negative connection between high PTTG1 expression and the -log10(IC50) values of 22 drugs (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA, Supplementary table 4), particularly MEK1/MEK2 inhibitors such as RDEA119, trametinib, CI-1040, selumetinib, and PD-0325901 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eB). These data showed that increasing PTTG1 could reduce drug sensitivity for these small compounds. PTTG1 showed a substantial positive correlation with the -log10(IC50) values of 13 drugs (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA, Supplementary table 4), including NSC-207895 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eC) and ABT-263 (Navitoclax) (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eE). The results suggested that these 13 small compounds have the potential to be PTTG1 target medicines. A molecular docking model was developed to investigate the mechanism of interaction between PTTG1 and prospective target medicines. We discovered that NSC-207895 could bind to PTTG1 and remain in the binding pocket surrounded by critical residues (Lys1189, Pro1144, Leu1140, His1142, Cys1160, and Arg1197) (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eD). ABT-263 could attach to PTTG1 and remain in the binding pocket surrounded by critical residues (Ala1671, Arg1675, Cys928, Glu925, Ala929, Leu945, Arg1638, Lys1645, Asp941, Gly931, Gln933, and Gln1674) (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eF). Our findings suggested that PTTG1 could be a therapeutic target for MEK1/MEK2 inhibitor resistance. ABT-263 and NSC-207895 may be effective anticancer drugs that target PTTG1.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n \u003ch2\u003eValidation of PTTG1 expression and function in NSCLC\u003c/h2\u003e\n \u003cp\u003eWe further confirmed the expression of PTTG1 in tumours by utilising the GEO database. The findings showed that NSCLC tissues had considerably greater levels of PTTG1 expression than did normal lung tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eA). Additionally, we used the online Kaplan Meier plotter database to determine the predictive significance of PTTG1 in 1161 patients with LUAD. Poor patient survival was observed to be substantially correlated with high expression of PTTG1 (low vs. high: 110.27 vs. 60.73, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eB). In the multi-drug resistance lung cancer dataset GSE77209, which included the parental cell lines H1299, H1355, and paclitaxel-carboplatin-resistant cells, we examined the expression of PTTG1. In H1299 and H1355, resistant cells expressed more PTTG1 than the parental cells did (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eC). The expression of PTTG1 in chemoresistance cells was confirmed by qRT-PCR. The expression levels of PTTG1 in the paclitaxel-resistant cell lines A549-TXR and H358-TXR were much higher than those in the parental cells, A549 and H358 (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eD). We employed short interfering RNAs to knock down PTTG1 in A549- TXR and H358- TXR in order to ascertain the biological roles of PTTG1 in paclitaxel-resistant cells (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eD). The findings showed that PTTG1 knockdown reduced A549-TXR and H358-TXR\u0026apos;s capacity to generate clones (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eF). The transwell experiment verifies that PTTG1 knockdown prevents A549-TXR and H358-TXR cells from migrating (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eG). These results imply that PTTG1 is involved in both chemoresistance and carcinogenesis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHuman life is seriously threatened by cancer. It is useful to employ biomarkers that are often expressed in many tumour types as targets for treatment and as tumour biomarkers. PTTG1 is involved in the growth of tumours and may function as an oncogene in the development and growth of tumours \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. We analyzed the role of PTTG1 in pan-cancer and confirmed that PTTG1 was overexpressed in 15 of 33 tumor types of TCGA datasets. The expression of PTTG1 was related to tumor classification in several cancers like ACC, LUAD, LIHC, BRCA, KIRC and KIRP. Moreover, high PTTG1 expression was related to poor patient survival, consistent with the previously reported role of PTTG1 in kidney renal clear cell carcinoma \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and hepatocellular carcinoma \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. An increasing number of research have investigated the relationship between genetic changes and cancer progression. We discovered that PTTG1 gene amplification resulted in significant protein expression in most tumours. Pathway enrichment revealed that PTTG1 comparable genes were involved in a variety of pathways, including cancer-related pathways such as 'cell cycle' and 'DNA replication'. These findings imply that PTTG1 has the potential to serve as a tumour diagnostic and prognostic marker. Furthermore, inhibiting aberrant PTTG1 expression at the genetic and protein levels could be a possible therapeutic method for reversing carcinogenesis.\u003c/p\u003e \u003cp\u003eTumor-produced chemicals and non-cancerous elements make up the tumour microenvironment. It is essential for the initiation, growth, metastasis, and reaction to treatment of tumours. One of the main mechanisms of tumour cell immune escape that leads to immunological dysfunction in cancer patients is T cell depletion\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Recent research has shown that T cell exhaustion is considered a mechanism of resistance for cellular immunotherapies \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and a significant marker of poor outcomes in many cancers including breast cancer \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and renal cell carcinoma \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Revitalization of exhausted T cells might improve immunity. In this context, recovered fatigued T cells may be a useful source of predictive biomarkers for a possible target. revitalising exhausted T cells might improve immunity. In this context, recovered fatigued T cells may be a useful source of predictive biomarkers for a possible target. In this study, we discovered that PTTG1 is positively connected with T cell exhaustion but negatively correlated with the ImmunoScore, StromalScore, and ESTIMATEScore in most types of tumours. Furthermore, PTTG1 was shown to be strongly expressed in immune-modulating cells such as Treg and Tprolif cells from NSCLC, KIRC, and LIHC patients. Treg cells produce an immuno-suppressive environment and are less sensitive to immune checkpoint inhibitors\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. It was noteworthy that Tprolif cells were able to exhibit high expression levels of immunological exhaustion markers, including PDCD1, HAVCR2, CTLA4, LAG3, and TIGIT\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In both KIRC and LIHC, our results demonstrated a strong positive correlation between PTTG1 and the immune-suppressive genes HAVCR2, CTLA4, LAG3, TIGIT, and PDCD1. These findings imply that the immune-suppressive microenvironment and T cell exhaustion may be connected to the elevated expression of PTTG1.\u003c/p\u003e \u003cp\u003eThe immuno-suppressive tumour microenvironment makes immune checkpoint drugs significantly less effective than anticipated in the treatment of cancer\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. To investigate PTTG1's involvement in immunotherapy resistance, the link between PTTG1 and immunotherapy response was examined. According to the results, patients with KIRC and melanoma undergoing immune checkpoint inhibition (anti-PD1) and adoptive T cell treatment (ACT) had a worse prognosis when their expression of PTTG1 was elevated. An important part of the antitumor immune system is played by cytotoxic T lymphocytes (CTLs). Immunotherapy resistance and tumour immune evasion are significantly aided by CTL malfunction. We discovered that higher CTL levels predicted longer patient life only when PTTG1 expression was low in glioma, myeloma, NSCLC, COAD, and KIRC. As a result, increased PTTG1 levels in tumours will reduce the positive correlation between CTL and survival. TMB and HRD are prognostic indicators for immune checkpoint inhibitor clinical effectiveness\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Particularly in KICH, we discovered a significant positive relationship between PTTG1 and TMB or HRD scores in most malignant tumours. These findings imply a relationship between tumour immunotherapy resistance and PTTG1 overexpression. The process might be connected to PTTG1's role in increasing cytotoxic T lymphocyte malfunction and mediating T cell exhaustion. It is possible to view PTTG1, which is expressed on Treg and Tprolif cells, as a novel therapeutic target to overcome immunotherapy resistance.\u003c/p\u003e \u003cp\u003eA increasing amount of research indicates that PTTG1 plays a crucial role in chemosensitivity\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Pancreatic ductal adenocarcinoma patients with decreased PTTG1 have good treatment response with great sensitivity and selectivity\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In ovarian cancer cell lines, downregulating PTTG1 improved saracatinib sensitivity\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. PTTG1 gene suppression reduces prostate cancer cell sensitivity to paclitaxel-induced apoptosis\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Consistent with earlier research, we found that PTTG1 was linked to chemosensitivity, particularly in MEK1/MEK2 inhibitors such as PD-0325901, RDEA119, trametinib, CI-1040, and selumetinib. ABT-263 and NSC-207895 may be employed as an efficient anticancer medication by targeting PTTG1, according to the molecular docking studies. Furthermore, we confirmed the expression and function of PTTG1 in NSCLC. We discovered that PTTG1 was considerably greater in NSCLC tissues compared to non-tumor lung tissues. Furthermore, elevated PTTG1 expression was found to be substantially associated with poor patient survival in NSCLC. PTTG1 expression levels in paclitaxel-resistant cell lines A549-PTX and H358-PTX were much higher than in the parental cells A549 and H358, which is consistent with recent studies in LUAD\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. PTTG1 knockdown inhibited clone formation and migration ability of A549- TXR and H358- TXR cells.\u003c/p\u003e \u003cp\u003eTo summarise, our investigations indicate that PTTG1 may have potential as a tumour diagnostic, prognostic, and chemosensitivity marker. Reversing carcinogenesis and chemoresistance may be achieved by blocking aberrant PTTG1 expression at the genetic and protein levels. Increased PTTG1 expression is associated with resistance to tumour treatment. The process might be connected to PTTG1's role in increasing cytotoxic T lymphocyte malfunction and mediating T cell exhaustion. It is possible to view PTTG1, which is expressed on Treg and Tprolif cells, as a novel therapeutic target to overcome immunotherapy resistance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdrenocortical carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBLCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBladder Urothelial Carcinoma BRCA,Breast invasive carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCESC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCervical squamous cell carcinoma and endocervical adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHOL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCholangio carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColon adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDLBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLymphoid Neoplasm Diffuse Large B-cell Lymphoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEsophageal carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlioblastoma multiforme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHNSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHead and Neck squamous cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKICH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney Chromophobe\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKIRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney renal clear cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKIRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney renal papillary cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAML\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Myeloid Leukemia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrain Lower Grade Glioma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLIHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLiver hepatocellular carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLUAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLung adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLUSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMESO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMesothelioma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOvarian serous cystadenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePancreatic adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePheochromocytoma and Paraganglioma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eREAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRectum adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSARC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSarcoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSKCM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSkin Cutaneous Melanoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStomach adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTGCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTesticular Germ Cell Tumors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTHCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThyroid carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTHYM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThymoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUCEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUterine Corpus Endometrial Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUterine Carcinosarcoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUVM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUveal Melanoma.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLihui Wang and Chunlin Zou designed the study. Handong Wei, Yashu Ma and Shuxing Chen collected the data. All Authors analyzed the data and wrote the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Joint Project on Regional High-Incidence Diseases Research of Guangxi Natural Science Foundation under Grant (No. 2023GXNSFAA026338), the National Natural Science Foundation of China (81803564), and the National College Students Innovation and Entrepreneurship\u0026nbsp;Training Program (No. 202210598040；No. 202210598038).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYanagida, M. (2005). 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Cell Rep \u003cem\u003e19\u003c/em\u003e, 1669-1684. 10.1016/j.celrep.2017.04.077.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PTTG1, Tumor immunity, Immunotherapy, Drug sensitivity, Pan-cancer","lastPublishedDoi":"10.21203/rs.3.rs-4923978/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4923978/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePituitary tumor-transforming gene 1 (PTTG1) is an important gene in tumour development. However, the relevance of PTTG1 in tumour prognosis, immunotherapy response, and medication sensitivity in human pan-cancer has to be determined.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTIMER, GEPIA, the human protein atlas, GEPIA, TISCH2, and cBioportal examined the gene expression, protein expression, prognostic value, and genetic modification landscape of PTTG1 in 33 malignancies based on the TCGA cohort. The association between PTTG1 and tumour immunity, tumour microenvironment, immunotherapy response, and anticancer drug sensitivity was investigated using GSCA, TIDE, and CellMiner CDB. Molecular docking was used to validate the possible chemotherapeutic medicines for PTTG1. Additionally, siRNA-mediated knockdown was employed to confirm the probable role of PTTG1 in paclitaxel-resistant cells.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePTTG1 is overexpressed and associated with poor survival in most tumors. Functional enrichment study revealed that PTTG1 is involved in the cell cycle and DNA replication. A substantial connection between PTTG1 expression and immune cell infiltration points to PTTG1's possible role in the tumour microenvironment. High PTTG1 expression is associated with tumour immunotherapy resistance. The process could be connected to PTTG1, which mediates T cell exhaustion and promotes cytotoxic T lymphocyte malfunction. Furthermore, PTTG1 was found to be substantially linked with sensitivity to several anticancer medications. Suppressing PTTG1 with siRNA reduced clone formation and migration, implying that PTTG1 may play a role in paclitaxel resistance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePTTG1 shows potential as a cancer diagnostic, prognostic, and chemosensitivity marker. Increased PTTG1 expression is linked to resistance to cancer treatment. The mechanism could be linked to PTTG1's role in promoting cytotoxic T lymphocyte dysfunction and mediating T cell exhaustion. It is feasible to consider PTTG1, which is expressed on Treg and Tprolif cells, as a new therapeutic target for overcoming immunotherapy resistance.\u003c/p\u003e","manuscriptTitle":"Multi-omics analysis Identifies PTTG1 as a prognostic biomarker associated with immunotherapy and chemotherapy resistance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-16 11:17:09","doi":"10.21203/rs.3.rs-4923978/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-19T10:28:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-19T04:04:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-19T04:03:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2024-08-16T09:18:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55b1553d-0d6a-4065-bef4-c67290f7b2e4","owner":[],"postedDate":"September 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T16:09:34+00:00","versionOfRecord":{"articleIdentity":"rs-4923978","link":"https://doi.org/10.1186/s12885-024-13060-5","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2024-10-25 15:58:17","publishedOnDateReadable":"October 25th, 2024"},"versionCreatedAt":"2024-09-16 11:17:09","video":"","vorDoi":"10.1186/s12885-024-13060-5","vorDoiUrl":"https://doi.org/10.1186/s12885-024-13060-5","workflowStages":[]},"version":"v1","identity":"rs-4923978","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4923978","identity":"rs-4923978","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}