Pancancer analysis of DCTPP1 and the impact of the combined knockdown of DCTPP1 with docetaxel on breast cancer

Pancancer analysis of DCTPP1 and the impact of the combined knockdown of DCTPP1 with docetaxel on breast cancer | 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 Article Pancancer analysis of DCTPP1 and the impact of the combined knockdown of DCTPP1 with docetaxel on breast cancer Pengfei lu, pengjie fu, mengxiao tian, Ibibulla Nurbiya, Huifang Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8065730/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction : Examine the carcinogenic function of DCTPP1 in breast cancer. Methods : A pancancer study revealed that high DCTPP1 expression was associated with poor prognosis. In MCF-7 cells, DCTPP1 knockdown (si-DCTPP1) was assessed via MTT, qRT‒PCR, Western blot, adhesion, scratch, and Transwell assays. In vivo tumorigenesis was used to measure tumor growth in vivo. DCTPP1 and EMT markers were detected via immunohistochemistry. Results : DCTPP1 was elevated in all tumor types and was linked to negative outcomes. In breast cancer, high DCTPP1 expression was associated with poor prognosis and possible immune evasion, as determined via bioinformatics analysis. si-DCTPP1 suppressed invasion, migration, and proliferation by increasing E-cadherin, decreasing TGF-β, and inhibiting cell adhesion. The combination of si-DCTPP1 and docetaxel synergistically intensified these effects. In vivo, si-DCTPP1 decreased tumor weight and growth. si-DCTPP1 synergistically reduced E-cadherin and TGF-β expression. Conclusion : DCTPP1 stimulates malignant characteristics in breast cancer. Its knockdown enhances docetaxel efficacy, indicating that DCTPP1 is a possible therapeutic biomarker. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Oncology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Globally, breast cancer has an exceptionally high incidence worldwide, accounting for almost one-third of all malignant tumors in women and 15% of all malignant tumors detected in female patients[ 1 ]. Standard treatment protocols for breast cancer include surgery, radiation, chemotherapy, or combination therapies, with chemotherapy serving as the mainstay of systemic therapy[ 2 ]. Despite improvements in overall survival rates for patients with breast cancer, problems such as tumor recurrence, medication resistance, and distant metastasis still have a major influence on patient outcomes[ 3 ]. The main mechanism of chemotherapy involves DNA damage-induced cell death. Thus, DNA repair pathways remain a critical focus in the exploration of chemotherapy resistance. Given the intricate molecular pathways driving breast cancer development, recurrence, drug resistance, and metastasis, finding novel prognostic biomarkers and therapeutic targets could significantly improve patient outcomes[ 4 ]. DCTPP1 is found on chromosome 16 and is encoded by deoxycytidine triphosphate pyrophosphatase 1 (DCTPP1) [ 5 ]. This enzyme is a member of the nucleotide triphosphate superfamily [ 6 ], specifically functioning as a dCTP hydrolase that removes aberrant dCTP molecules to preserve the stability of the deoxyribonucleotide triphosphate (dNTP) pool. Its hydrolytic action is essential for ensuring precise DNA replication and stable genomic expression. Numerous studies have suggested that DCTPP1 may be a potential target for anticancer medications, with emerging evidence highlighting its major influence on tumor growth, chemotherapy resistance, and prognosis prediction. Consequently, DCTPP1 has become a promising nucleotide metabolism-related target in cancer therapy [ 7 ]. However, its precise function in breast cancer progression remains unclear. EMT (epithelial‒mesenchymal transition) is crucial for tumor metastasis and invasion and can even affect resistance to treatment. As a dynamic and intricate process, EMT involves the loss of epithelial cells and the development of mesenchymal cell traits. Tumor metastasis begins with EMT, resulting in distant spread through limited expression of E-cadherin and high expression of cadherins such as N-cadherin [ 8 ]. Treatment strategies are much more difficult in this metastatic phase. EMT transcription factors such as TGF-β, Twist, and ZEB can induce EMT development and increase tumor spread. Studies have shown that EMT is linked to treatment resistance in a number of cancer types. Recent research suggests that EMT-related transcription factors may increase radioresistance and chemotherapy resistance by interfering with DNA repair processes. For example, TGF-β hinders homologous recombination-dependent DNA repair [ 9 – 11 ], although more research is needed to understand its molecular mechanisms. This study revealed that DCTPP1 is highly expressed in BRCA and is positively correlated with EMT pathway activation. As a tumor-promoting gene, its low expression increases the effectiveness of docetaxel in suppressing breast cancer. In vitro experiments demonstrated that DCTPP1 knockdown inhibited MCF-7 cell proliferation, adhesion, metastasis, and invasion. In vivo studies further confirmed that decreased DCTPP1 expression successfully inhibited the growth of breast cancer tumors. Results DCTPP1 expression patterns and clinical relevance in breast cancer and pancancer The TIMER and UALCAN datasets were used for pancancer analysis of DCTPP1 expression differences between neighboring and malignant tumor tissues. The results revealed significant upregulation of DCTPP1 in 14 tumor types (BRCA, BLCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRP, LIHC, LUAD, LUSC, STAD, and UCEC), whereas DCTPP1 expression was downregulated in 3 tumor types (KICH, KIRC, and THCA) (Fig. 1 and Fig. S1 ). Analysis of TCGA BRCA data revealed significantly greater DCTPP1 expression in malignant tissues than in normal tissues (P < 0.001) (Fig. 1 ). DCTPP1 expression varied across normal tissues and four breast cancer stage groups according to the UALCAN study. (Normal vs. Stage 1–4: P < 0.0001; Stage 1 vs. Stage 3: P < 0.0001) (Fig. 1 ). Moreover, DCTPP1 exhibited distinct expression patterns between normal tissues and 3 kinds of breast cancer: the normal and luminal subtypes (P < 0.0001), the normal and HER2-positive subtypes (P < 0.0001), and the normal and TNBC subtypes (P < 0.0001). Notably, Fig. 1 D shows a significant difference between the luminal subtype and TNBC (P < 0.0001). Table 1 Legend (350 words max). Example legend text. Condition n p A 5 0.1 B 10 0.01 According to imaging data from the Human Protein Atlas, normal breast tissue has modest expression of DCTPP1. However, breast cancer tissue has much higher levels (left: Patient ID#2773; right: Breast cancer tissue, Patient ID#1874). K‒M curves generated via GEPIA were used to analyze the overall survival (OS) and disease-free survival (DFS) of breast cancer patients in the high- and low-DCTPP1 expression groups (Fig. 1 ). In pancancer prognostic forest models for DCTPP1, according to the OS forest plot, DCTPP1 acts as a risk factor in 12 tumor types (BRCA, GBMLGG, LGG, TARGET-LAML, SARC, LIHC, SKCM, SKCM-M, TARGET-NB, PAAD, LAML, and TARGET-ALL-R) while protecting against seven different forms of tumors (UCEC, STES, KIPAN, COADREAD, STAD, KIRC, READ). Interestingly, BRCA was associated with OS (HR of 1.67) and DFS (HR of 0.17). The DFS forest plot revealed that DCTPP1 has an adverse effect on prognosis when it is highly expressed in two tumor types (LIHC and PAAD), whereas poor prognosis is associated with low expression of DCTPP1 in STES (Fig. 1 ). The Sangerbox online platform was used to examine the relationship between DCTPP1 expression and immune cell profiles across cancers. DCTPP1 expression and CD4 + T cells are negatively correlated in the majority of tumor types. In PCPG, KIRC, LIHC, PRAD, THCA, and PAAD, DCTPP1 exhibited favorable relationships with 3 or more immune cell types. Conversely, in BRCA, LUSC, ESCA, SKCM, and DLBC, it exhibited negative relationships with 3 or more immune cell types. DCTPP1 is negatively correlated with B cells, CD4 + T cells, CD8 + T cells, and neutrophils in breast cancer (Fig. 1 ). The TIMER platform confirmed a negative association between DCTPP1 expression and CD8 + T cells (Fig. 1 ). Furthermore, DCTPP1 expression was negatively related to both the stromal score and immune score, in addition to the ESTIMATE score, with P < 0.0001 (Fig. S1 ). Functional prediction of DCTPP1 To clarify the DCTPP1-related signaling mechanisms in breast cancer, we performed GO, KEGG and GSEA. These findings indicate that DCTPP1 is strongly involved in chromatin remodeling, DNA repair, and protein ubiquitination. Cellular components are found primarily in the nucleus and cytoplasm, whereas molecular activities include protein binding and metal ion binding (Fig. 2 ). The KEGG results revealed that DCTPP1 was enriched in tumor-associated mTOR signaling pathways. GSEA further confirmed that DCTPP1-related genes are positively related to DNA repair (Fig. 2 ). Coremine suggested that the DCTPP1 gene may be involved in a number of biological processes and pathways, such as nucleotide metabolism, DNA demethylation, and WNT signaling pathways, which play important roles in tumor development. The DCTPP1 gene may be associated with a number of illnesses, including breast cancer, pre-B-cell lymphoblastic leukemia, and ovarian malignancies (Fig. S1 ). To elucidate the molecular regulatory mechanisms of DCTPP1, we mined GeneMINA for its interaction links and identified 11 interacting genes. Interestingly, DCTPP1 exhibited stronger physical interactions with CIAPIN1 and NUBP (Fig. 2 ). Mapping PPIs from the STRING database with Cytoscape revealed 10 interacting proteins that cover DNA synthesis/repair (RRM1, RRM2), nucleotide metabolism (AK9, DCTD), deoxyribonucleotide salvage pathway (DCK), and cell cycle regulation (NME1, NME2) (Fig. S1 ). In contrast to that in normal breast tissue and non-NRF2-altered samples, DCTPP1 expression was significantly greater in samples with altered NRF2 signaling (normal vs. NRF2-altered samples, P < 0.05; normal vs. NRF2-altered samples, P < 0.0001). Furthermore, DCTPP1 was highly expressed in specimens whose RTK pathways, p53/Rb pathways, WNT pathways, and mTOR pathways were altered, all of which were significantly greater than those in normal breast tissue and pathway-altered samples (Fig. 2 and Fig. S1 ). To investigate the mutational features of DCTPP1, we examined its mutation and expression patterns in Sangerbox. Significant changes were found in 15 tumor types: BRCA, CESC, LUAD, COAD, ESCA, STES, SARC, KIPAN, STAD, PRAD, HNSC, LUSC, LIHC, OV, and BLCA, with BRCA primarily showing gain-of-function (gain-of-function) mutations (Fig. 2 ). By excluding samples with wild-type mutations from the BRCA cBioPortal database, we identified 51 breast cancer mutation cases, revealing both frameshift mutations and multiple-frame mutations in DCTPP1 (Fig. 2 ). DCTPP1 and chemotherapeutic drug sensitivity prediction Analysis of DCTPP1 expression and its correlation with chemotherapeutic sensitivity in breast cancer revealed no statistically significant associations (Fig. 3 ). The correlations, although statistically insignificant, were weakly negative for camptothecin (n = 2 570, R=-0.019) and 5-fluorouracil (n = 1 875, R=-0.012) and weakly positive for selumetinib (n = 3 452, R = 0.03). No correlation was detected for cyclophosphamide (n = 957, R=-0.003), paclitaxel (n = 1 353, R=-0.008), or cisplatin (n = 2613, R=-0.001). 3.4 Cellular level confirmation that DCTPP1 promotes the proliferation, migration and invasion of breast cancer cells DCTPP1 was knocked down in the MCF-7 cell line. Successful DCTPP1 knockdown in MCF-7 cells was verified by qRT‒PCR and Western blotting (Fig. 4 ). The MTT assay results demonstrated that DCTPP1 did not affect breast cancer cell proliferation (Fig. 4 ). Cell adhesion experiments revealed a significantly reduced adhesion capacity of breast cancer cells following DCTPP1 knockdown (Fig. 4 ). Wound healing and Transwell assays revealed that si-DCTPP1 dramatically reduced cell migration and invasion (Fig. 4 ). Previous studies have established the crucial role of EMT in tumor metastasis. We postulated that DCTPP may affect breast cancer cell migration and invasion through EMT. qRT‒PCR and Western blot analyses of E-cadherin and TGF-β expression revealed increased E-cadherin levels and decreased TGF-β expression in DCTPP-deficient cells. These results support a favorable connection between DCTPP1 and EMT (Fig. 4 ). At the cellular level, inhibition of DCTPP1 expression can enhance the inhibitory effect of docetaxel on breast cancer To assess the clinical effectiveness of DCTPP1 in breast cancer treatment, 3 experimental groups were created: the docetaxel treatment group, the si-NC combined with docetaxel treatment group, and the si-DCTPP1 combined with docetaxel treatment group. MTT assays revealed that the OD values in the docetaxel-knockdown group were substantially lower than those in the docetaxel-only group (Fig. 5 ). Cell adhesion assays revealed a marked decrease in cell adhesion capacity in the docetaxel + knockdown group (Fig. 5 ). Wound healing and Transwell assays revealed significantly reduced migratory and invasion capacities in these groups (Fig. 5 ). To examine whether DCTPP1 decreases the effectiveness of docetaxel through EMT, we used qRT‒PCR and Western blotting to measure the expression of DCTPP1, E-cadherin, and TGF-β. The results revealed that the DCTPP1 and TGF-β expression levels in the docetaxel + DCTPP1 group were dramatically lower than those in the other two groups, whereas E-cadherin expression tended to increase (Fig. 5 ). This study performed a comprehensive analysis of six experimental groups to investigate the connection between the tumor-suppressive effects of DCTPP1 and docetaxel. The MTT analysis demonstrated that the OD values in the si-DCTPP1 plus docetaxel group were dramatically lower than those in the Control, si-NC, and si-DCTPP1 groups, with noticeable reductions in the si-DCTPP1 plus docetaxel groups compared with the single-drug docetaxel group compared with the Control group or docetaxel in combination with the si-NC group (Fig. 6 ). The cell adhesion assay revealed that the OD values in the si-DCTPP1 plus docetaxel group were also significantly lower than those in the control, si-NC, and si-DCTPP1 groups, with more noticeable reductions than those in the single-drug docetaxel group versus the control group or docetaxel in combination with the si-NC group (Fig. 6 ). Both the cell scratch assay and Transwell assay indicated that the si-DCTPP1 plus docetaxel group demonstrated significantly lower scratch healing percentages than the control and si-NC groups did, with more noticeable decreases than those in the single-drug docetaxel group compared with the control group or the si-NC group combined with docetaxel. However, the scratch healing percentage of the si-DCTPP1 group did not differ significantly from that of the si-DCTPP1 plus docetaxel group. These results suggest that docetaxel may exert its effects by lowering DCTPP1 expression (Fig. 6 ). DCTPP1, E-cadherin, and TGF-β expression was markedly lower in the si-DCTPP1 plus docetaxel group than in the control and si-NC groups, according to qRT‒PCR analysis. Notably, DCTPP1 expression was lower in the docetaxel group than in the control group, while changes in E-cadherin and TGF-β levels were not statistically significant. The si-NC group demonstrated comparable outcomes to those of the si-NC plus docetaxel group. The expression of DCTPP1, E-cadherin, and TGF-β did not differ significantly between the si-DCTPP1 group and the knockdown plus docetaxel group (Fig. 6 ). Inhibition of DCTPP1 expression can inhibit breast cancer proliferation in vivo On the basis of the inhibitory effect of DCTPP1 on MCF-7 cell proliferation in vitro, we created a mouse model to validate its in vivo anti-breast cancer effectiveness. The findings demonstrated that tumors in mice transplanted with low-DCTPP1-expressing cells presented significantly decreased weight and shortened length (Fig. 7 ). To verify pertinent gene expression levels, immunohistochemical staining was performed on tumor sections (Fig. 7 ). E-cadherin expression was not significantly altered in DCTPP1-low-expressing animals. The TGF-β levels tended to decrease but lacked statistical significance. In the si-DCTPP1 plus docetaxel group, both E-cadherin and TGF-β levels tended to decrease (Fig. 7 ). Discussion Breast cancer has alwas been the primary source of disease burden among women. Low survival rates, high recurrence rates, and an adverse prognosis are common characteristics of advanced breast cancer. because of medication resistance [ 12 – 14 ]. When breast cancer spreads to surrounding tissues and lymph nodes, treatment efficacy is severely impaired. EMT (epithelial‒mesenchymal transition) is known to be a critical mechanism causing cancer metastasis and chemotherapy resistance, accelerating disease progression [ 15 – 17 ]. Notably, EMT and DNA damage are regulated in both directions: DNA damage triggers EMT, while EMT transcription factors (EMT-TFs) promote DNA repair through pathways that are regulated in both directions: the EGFR-Erk1/2/Akt-Rad51 axis [ 18 – 24 ]. DCTPP1 preserves dNTP pool homeostasis by hydrolyzing dCTP and its derivatives (e.g., 5-methyldCTP), preventing nucleotide metabolic imbalances that result in mistakes in DNA replication and genomic instability [ 25 – 26 ]. DCTPP1 influences numerous aspects of cancer progression, prognosis, and treatment results. For example, targeting DCTPP1 in colorectal cancer disrupts amino acid reprogramming for tumor suppression [ 27 ], enhances gastric cancer cell proliferation with prognostic correlations [ 28 ], and reverses cisplatin-induced resistance in ovarian cancer [ 7 ]. These results direct our research into whether DCTPP1 drives breast cancer malignancy by enhancing the EMT process. This study conducted a systematic bioinformatics investigation of DCTPP1 in multiple cancer and breast cancer subgroups, revealing its possible roles in tumor growth and immune microenvironment regulation. Previous research has indicated that DCTPP1 acts as a prognostic biomarker across cancers, is negatively correlated with sensitivity to different chemotherapeutic agents, and affects tumor growth by modifying the cell cycle and immune system [ 29 ]. Our research revealed that DCTPP1 is significantly upregulated in 14 types of tumors but downregulated in 3 types, with high DCTPP1 expression strongly linked to poor prognosis in breast cancer patients. This finding implies that DCTPP1 may serve as a significant prognostic factor at the pancancer level, which is consistent with previous findings [ 29 ]. Furthermore, high DCTPP1 expression is strongly correlated with immune cell infiltration across a variety of tumor types. Further exploration of its expression and function in breast cancer was performed. Previous reports indicate that DCTPP1 is extensively overexpressed in breast cancer (particularly the luminal A subtype), with copy number amplification being the primary mutation type, and its high expression is strongly associated with poor prognosis and treatment resistance [ 31 ]. Our team further demonstrated that DCTPP1 expression increases dramatically in different stages and molecular subtypes of breast cancer (Luminal, HER2+, TNBC). Subsequent mutation spectrum analysis revealed that after wild-type samples were removed, DCTPP1 mutations were primarily missense and multiallelic mutations. Misense mutations (e.g., HER2 missense mutations) have been shown to increase resistance to specific treatments in breast cancer cells [ 30 ]. High expression of DCTPP1 was negatively correlated with the breast cancer stromal score, immunoscore, and ESTIMATE score, in addition to being negatively correlated with CD8 + T-cell infiltration, neutrophil, B cell, and CD4 + T-cell infiltration. These findings suggest that DCTPP1 may inhibit immune cell infiltration and microenvironment activity, thereby facilitating immune evasion in breast cancer cells. Although the relationship between DCTPP1 expression and chemotherapeutic drug sensitivity was not statistically significant, it still exhibited weak negative correlations with camptothecin and 5-fluorouracil and weak positive associations with selumetinib. Functional enrichment analysis and molecular interaction network studies explored the possible pathways associated with DCTPP1 in breast cancer. GO and KEGG analyses revealed that DCTPP1 is involved in chromatin remodeling, DNA repair, and protein ubiquitination processes. GSEA also revealed that highly enriched DCTPP1-associated genes were significantly enriched in DNA repair pathways. GeneMINA and STRING database analyses revealed that DCTPP1 forms interaction networks with several DNA repair genes (e.g., RRM1 and RRM2) and that high expression of DNA repair genes (e.g., FANCF) promotes breast cancer cell growth [ 34 ]. Furthermore, the UALCAN platform indicated that DCTPP1 might stimulate tumor development cooperatively through key pathways associated with EMT, such as NRF2, RTK, WNT, p53/Rb, and mTOR. For example, NRF2 controls EMT by inhibiting Akt/GSK3β and maintaining redox equilibrium. [ 35 ]; RTK (e.g., EGFR) indirectly controls EMT via the PI3K/Akt/mTOR pathway; WNT uses β-catenin to directly activate EMT [ 36 ]; p53/Rb participates in EMT through cell cycle and apoptosis regulation; and mTOR influences EMT via the PPARγ-NRF2 and Wnt/PCP pathways [ 38 ]. Together, these five mechanisms work in concert to control EMT in breast cancer cells [ 37 ]. DCTPP1 is essential for preserving DNA replication accuracy and genomic stability [ 39 ]. Mounting evidence indicates that aberrant expression or mutation of DNA repair genes can control EMT-associated genes (e.g., TWIST1 and SNAIL1), thereby promoting breast cancer cell metastasis. For example, DNMT3A activates DNA demethylation to activate the transcription of TWIST1 and SNAIL1 [ 40 ]. CBX3 uses the ERK1/2 signaling pathway to control the expression of genes linked to EMT. [ 41 ]. On the basis of the regulatory antecedents involving the DNMT3A and CBX3 proteins, we speculate that DCTPP1 may regulate EMT in breast cancer and increase its metastatic invasiveness. Our data revealed that DCTPP1 knockdown decreases the growth of breast cancer cells, although the difference was not statistically significant. This decreasing trend is consistent with previous experimental findings, but earlier analyses used time series line plots without statistical examination [ 42 ]. The experimental results revealed that DCTPP1 knockdown decreases the cell adhesion capacity and profoundly reduces both invasion and metastasis. qRT‒PCR, Western blotting, and immunohistochemistry all verified that DCTPP1 knockdown increased E-cadherin expression while suppressing TGF-β. These results suggest that DCTPP1 knockdown can inhibit EMT to weaken the malignant biological activity of breast cancer. Docetaxel and paclitaxel belong to the taxane family, which stabilizes tubulin and causes tumor cells to enter the cell cycle. Docetaxel plays a prominent role in breast cancer treatment [ 43 ]; however, tumor cells activate EMT, increasing their resistance to docetaxel [ 44 – 45 ]. According to recent research, docetaxel resistance can be reversed by focusing on EMT. For example, recombinant methionine synthetase (rMETase) dramatically increases docetaxel's effectiveness against drug-resistant osteosarcoma and soft tissue sarcoma cells by inhibiting EMT-related mechanisms [ 46 ]. In esophageal squamous cell carcinoma, downregulation of NOTCH3 activates the EMT marker vimentin (VIM), resulting in treatment resistance to medications such as cisplatin [ 47 ]. CTEN upregulates TGF-β1 expression through demethylation, promoting EMT and increasing chemotherapy resistance in breast cancer (BC) cells [ 48 ]. Furthermore, DNA repair genes are reported to play important roles in chemotherapy resistance. KMT2C-mediated suppression of DNA damage repair-related genes enhances breast cancer sensitivity to chemotherapy following its knockdown [ 49 ]. DCTPP1 affects cisplatin resistance in ovarian cancer through PI3K/Akt signaling [ 50 ], although its impact on docetaxel resistance in breast cancer remains unknown. Our research team revealed for the first time that DCTPP1 expression decreases following docetaxel treatment. Subsequent studies revealed that coadministering docetaxel with DCTPP1 knockdown considerably increased E-cadherin expression and decreased TGF-β levels, with the most pronounced effects observed in the combination treatment group. Functionally, this combination strategy resulted in the most significant reduction in breast cancer proliferation, adhesion, migration, and invasion among all six groups, demonstrating significantly superior efficacy compared with the other treatments. Animal research revealed that tumors in mice implanted with DCTPP1-low-expressing MCF-7 cells presented marked weight reduction and shortened tumor length. Immunohistochemical examination revealed that DCTPP1-only knockdown mice presented no significant changes in E-cadherin expression, with the exception of a statistically negligible decrease in TGF-β. However, the E-cadherin and TGF-β levels decreased in the si-DCTPP1 and docetaxel cotreatment groups. The lack of statistically significant variations may reflect complex intracellular processes. Collectively, these findings suggest that DCTPP1 knockdown could reverse the EMT process in breast cancer cells, thereby increasing docetaxel sensitivity and synergistically decreasing malignant biological behaviors. This study performed comprehensive bioinformatics analysis and validated the findings through "in vitro-in vivo" multilevel experiments, elucidating the novel mechanism by which DCTPP1 drives EMT to increase breast cancer progression and docetaxel resistance. These results offer novel treatment targets and approaches to overcome chemotherapy resistance in breast cancer. Methods Genetic expression data Clinical information and gene expression profiles of patients with breast cancer from the TCGA database (normal samples, 113; cancer samples, 1,109) ( https://portal.gdc.cancer.gov/ ). Pancancer analysis Using the TIMER ( https://cistrome.shinyapps.io/timer/ ) and Sangerbox ( http://sangerbox.com/ ) online platforms, we examined the variations in DCTPP1 expression between tumor tissues and normal tissues in a range of malignancies. The OS and DFS forest plots of DCTPP1 expression and patient prognosis were created via Sangerbox. Analysis of DCTPP1 expression in breast cancer We analyzed the differences in the expression of DCTPP1 in breast cancer via R (4.4.1) and GEPIA ( http://gepia.cancer-pku.cn/ ). The Human Protein Atlas ( https://www.proteinatlas.org/ ) was utilized to obtain immunohistochemistry images of DCTPP1 in both normal breast tissue and breast cancer tissues. TIMER was used to analyze immune infiltration, whereas GEPIA was used to analyze OS and disease-free survival (DFS) with the Kaplan‒Meier method. Functional exploration of DCTPP1 We first investigated the pathways enriched with genes linked to DCTPP1. We determined the top 200 breast cancer-associated genes associated with DCTPP1 in GEPIA and identified genes with a PCC ≥ 0.3 from UALCAN, resulting in the identification of a total of 473 DCTPP1-related genes. DAVID ( https://david.ncifcrf.gov/ ), we acquired information from Gene Ontology (GO) and functional enrichment analysis (KEGG) data for these genes. Microarray analysis ( https://www.microbioinfo.com/ ) was performed to screen genes with counts > 5 and p 0.3 between DCTPP1-related genes and breast cancer, along with pathway data from GSEA ( https://www.gsea-msigdb.org/gsea/index.jsp ). Finally, GSEA maps showing the involvement of DCTPP1 in breast cancer were created via GSEA software. Second, we built the interaction network of DCTPP1. GeneMAN ( https://www.genemina.org/ ) was used to construct the gene network of DCTPP1. From STRING ( https://cn.string-db.org/ ), we mapped the PPI network of DCTPP1 via Cytoscape 3.10.3 after the protein interaction data were downloaded. Finally, Sangerbox performed a pancancer gene mutation analysis of DCTPP1. cBioPortal uses R to create a mutation waterfall graph after the gene mutation data for DCTPP1 in breast cancer are downloaded. Prediction of the effect of DCTPP1 on drug sensitivity in breast cancer The GDSC drug sensitivity data and TCGA clinical data were downloaded. ( https://www.cancerrxgene.org/ ). Box plots and drug sensitivity correlation diagrams were drawn via R (4.4.1). Cell culture and DCTPP1 knockdown MCF-7 cells were purchased from Procell Life Science & Technology Co., Ltd. MCF-7 cells were cultivated in DMEM supplemented with 1% penicillin/streptomycin and 10% fetal bovine serum (FBS). The cells were kept in a cell culture incubator at 37°C with 5% CO 2 . To create a stable transfection line with low DCTPP1 expression, MCF-7 cells were transfected with the pLVX-ShRNA2-Puro plasmid expression vector. The matching empty vector group served as a control. The supplier of the pLVX-ShRNA2-Puro-Homo-DCTPP1-479 plasmid was Wuhan Bafei Biotechnology Service Co., Ltd. For temporary transfection, 10 µL of viral mixture was applied to each well according to the manufacturer's instructions. Following a 24 h incubation period at 37°C, fresh media was added. At 48 h post infection, the medium was replaced with medium containing 2 µg/mL puromycin, and then, the medium was replaced with new medium containing 2 µg/mL puromycin every 2–3 days, resulting in many dead cells. This process was repeated until the antibiotic-resistant colonies were identified. Real-time fluorescence quantitative PCR (qRT‒PCR) After TRIzol reagent was used to lyse the sample cells, 200 µL of chloroform was added. After mixing and incubation at room temperature for 5 min, the layers were separated by centrifuging the mixture. The RNA-containing aqueous phase (approximately 400 µL) was combined with 400 µL of isopropanol for RNA precipitation. Following a 75% ethanol wash, the mixture was centrifuged, dried, and dissolved in 20 µL of DEPC buffer. The RNA purity and concentration were calculated by measuring the OD260, OD280, and OD260/OD280 values of a 2 µL sample of the RNA mixture via a microplate reader. Reverting was carried out via HiScript® II Q RT SuperMix for qRT‒PCR (with gDNA wiper) on the basis of the determined RNA concentration, followed by qRT‒PCR with VAZYME SYBR Green Master Mix. The 2 -△△Ct method was used to examine the experimental results. The experimental results were analyzed via the 2 -△△Ct method. Supplementary Table 1 contains a list of the qRT‒PCR primers used. Western blot One milliliter of RIPA buffer was prepared, which contained 10 µL of 100 mM PMSF and 10 µL of phosphatase inhibitor. Following the completion of cell growth in six-well plates, 120 µL of PMSF-containing lysis buffer was added to each well, and the mixture was subjected to ice-cold lysis for 30 minutes. After the cell lysate was collected, the sample was diluted ten times. The protein concentration was determined via a BCA protein assay kit (Beyotime). The cell lysate was then combined with 5× protein loading buffer at a 4:1 volume ratio and cooked at 100°C for 10 minutes to denature the proteins. The mixture was subjected to SDS‒PAGE, followed by transfer to a PVDF membrane. The membrane was incubated with 5% skim milk at room temperature for 2 h to prevent nonspecific binding. Primary antibodies (β-actin, TGF-β, DCTPP1, and E-cadherin, diluted 1:1,000) were incubated with the samples overnight at 4°C. The following day, secondary antibodies (HRP-labeled, 1:10,000 dilution) were applied, and the immunoblot signals were visualized via the enhanced chemiluminescence (ECL) method. Supplementary Table 2 contains comprehensive details regarding the antibodies used in this experiment. MTT assay MCF-7 cells were plated at 5×10 3 cells per well in a 96-well plate. Following cell attachment, the medium was removed according to the manufacturer’s instructions and replaced with MTT reagent. The plate was incubated in an incubator for 4 h. After the medium was aspirated, 150 µL of DMSO was added, and the mixture was shaken for 10 min. Finally, a microplate reader (Molecular Devices) was used to measure the absorbance at OD568. Cell adhesion assay They applied a 100 µg/mL fibrinonectin dilution to a 96-well plate. Each well received 100 µL of the MCF-7 cell suspension at 5×10 5 /mL. After 2 min of low-speed centrifugation to allow for cell sedimentation, the plates were incubated for 30 min at 37°C with 5% CO 2 . Aspiration of the detached cells was followed by two washes with PBS. A control group containing normal cells (without cell removal) was created. One hundred microlitres of culture material was added to each well, and images were taken at 20× magnification. The absorbance was then measured via the MTT technique. Wound healing assay Six-well plates were inoculated with approximately 5×10 5 cells (digested and counted). The cells were cultivated overnight at 37°C and 5% CO 2 saturation with humidification. Using a handle tip against a ruler, a scratch was made as close as possible to the back crossbar. After the scratched areas were removed and the cells were washed 3 times with PBS, serum-free media was added. At 0 h, pictures were taken at 10x magnification. The plates were then cultivated in an incubator set at 37°C with 5% CO 2 . Images were taken again at 10x magnification after a 24 h period. Transwell assay MCF-7 cells were diluted to 3×10 5 /mL in DMEM. A 24-well plate was filled with 800 µL of 10% FBS DMEM (including biotin) that had been prechilled to 4°C. Transwell chambers were inserted. At the middle of the bottom surface of the upper chamber, 100 µL of Matrigel with a final concentration of 1 mg/mL was added vertically. For four to five h, the mixture was incubated at 37°C until gelatinization occurred. Once gelatinized, 200 µL of each cell suspension (3×10 5 /mL) was transferred into the transwell top chamber. For 24 h, the mixture was cultivated at 37°C with 5% CO 2 . The transwell was removed, the chamber was gently washed with PBS, and the cells were fixed with 70% ice-cold ethanol for 1 h. Sterile cotton balls were used to remove any unmigrated cells from the top chamber side after they were stained with 0.5% crystal violet solution and incubated for 20 min at room temperature. The cells were rinsed with PBS. The samples were examined and imaged using a Nikon inverted microscope. Mice and tumor inoculation In compliance with ethical standards, twelve female BALB/c mice aged 5–6 weeks were acquired fromSpeFud (Beijing) Biotechnology Co., Ltd. and kept at the Experimental Animal Center of the First Affiliated Hospital of Xinjiang Medical University. The researchers anesthetized the mice with 5% sodium pentobarbital at a dose of 55 mg/kg via intraperitoneal injection.The DCTPP1 interference agent was administered subcutaneously to both control and transformed cells. (0.2 mL, 5×10 6 ) were given under the left flank of the BALB/c mice. After tumor modeling, the mice were randomly split into four groups, with three in each group: normal cells, normal cells + docetaxel, si-DCTPP1, and si-DCTPP1 + docetaxel. The normal cells + docetaxel and si-DCTPP1 + docetaxel groups were administered 5 mg/kg docetaxel intraperitoneally. The normal cells and si-DCTPP1 groups were administered 0.2 mL of saline solution via intraperitoneal injection once daily for five consecutive 2 d. After that, the tumors were removed, captured on a camera, weighed, and kept as specimens embedded in paraffin. Finally,use the right hand tograsp the base of the mice’s tail and lift it, place it firmly on the operating table, fix its head/neck with the left thumb and index finger, and then pull the tail quickly backward and upward to achieve instantaneous cervical dislocation, resulting in immediate unconsciousness.Statistical analysis was performed on tumor portions, and slices of the tumors fixed in paraffin were subjected to immunohistochemical staining.All animal experiments were conducted in accordance with the ARRIVE 2.0guidelines and have been approved by the relevant animal ethics review committee. Immunohistochemistry (IHC) The tissue slices were subjected to heat-induced antigen retrieval via sodium citrate buffer (pH = 6.0) at 98–100°C. The slides were allowed to cool naturally before being gently agitated on a decolorization shaker for 3 cycles of five-minute rinses in PBS (pH = 7.4). After they had dried somewhat, the serum was applied to the slides and incubated at 37°C in a humid box for 30 min. PBS (pH = 7.4) was used 3 times to rinse the slides for 3 min each time. Primary antibodies (DCTPP1: 1:20,000 dilution; E-cadherin: 1:100 dilution; TGF-β: 1:500 dilution) were added, and the samples were incubated in a humid box overnight. After the slides were rinsed with PBS, they were rewarmed and coated with MaxVision secondary antibody. The slides were incubated in a humid box at room temperature for 20–30 min, followed by rinsing with PBS. DAB staining was completed, and when coloration was observed, the stain was quickly removed with tap water. After 3 min of hematoxylin staining, 1% hydrochloric acid alcohol was used to dehydrate the samples. Microscopic inspection was performed to assess the staining intensity. The slides were rinsed with tap water for 10 min. For 5 min each, the samples were submerged in 75%, 85%, 95%, 100%, and 100% alcohol I and II. The slides were then processed in xylene for 3 min twice, followed by mounting with neutral glue. The antibodies used for IHC are listed in Supplementary Table 3. Statistical analysis Statistical analysis and visualization were performed via R (4.4.1) and GraphPad Prism (10.1.2). All experimental data were analyzed via GraphPad Prism (10.1.2). For normally distributed quantitative data, the means ± standard deviations were calculated via one-way ANOVA for multigroup analysis and independent samples t tests for pairwise comparisons. SNK tests were employed for post hoc comparisons when significant differences were found. Mann‒Whitney U tests for pairwise comparisons, nonparametric Kruskal‒Wallis tests for multigroup comparisons, and median values (quartile ranges) were used to assess nonnormal distributions. Categorical data are expressed as frequencies, rates, or proportions, with χ² tests applied. Every experiment was carried out 3 times. P < 0.05 indicated statistically significant results. We created drug sensitivity analyses, gene mutation waterfall diagrams, and expression variation maps of DCTPP1 in breast cancer using R. Gene mutation frequencies were calculated as frequency = (number of samples with specific mutations/total samples) ×100%. Regular expressions were used to categorize the different types of mutations. T test comparisons were performed between the high-expressing and low-expressing groups regarding drug sensitivity measures (IC50, AUC). Cohen's d effect size was calculated to measure intergroup significance, with thresholds: 0.5 for large effects. Multiple testing correction (Benjamini‒Hochberg method) was performed to control false positive rates in multiple drug tests, and p-adjust was used to calculate the corrected P value. Declarations LaTeX formats citations and references automatically using the bibliography records in your .bib file, which you can edit via the project menu. Use the cite command for an inline citation, e.g.2. For data citations of datasets uploaded to e.g. figshare , please use the howpublished option in the bib entry to specify the platform and the link, as in the Hao:gidmaps:2014 example in the sample bibliography file. Acknowledgements (n ot compulsory) Acknowledgements should be brief, and should not include thanks to anonymous referees and editors, or effusive comments. Grant or contribution numbers may be acknowledged. Author contributions statem ent Must include all authors, identified by initials, for example: A.A. conceived the experiment(s), A.A. and B.A. conducted the experiment(s), C.A. and D.A. analysed the results. All authors reviewed the manuscript. Additional information To include, in this order: Accession codes (where applicable); Competing interests (mandatory statement). The corresponding author is responsible for submitting a competing interests statementon behalf of all authors of the paper. 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Supplementary Files Supplementarytable1Listofprimersequences.docx Supplementarytable3ListofIHCAntibodyInform.docx Supplementarytable2ListofWBAntibodyInformation.docx SupplementaryFig.1.docx Supplementarytable1Listofprimersequences.docx Supplementarytable2ListofWBAntibodyInformation.docx Supplementarytable3ListofIHCAntibodyInform.docx bactin.jpg Ecsdherin.jpg DCTPP1.jpg TGFb.jpg WB.zip Cite Share Download PDF Status: Posted Version 1 posted 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. 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1","display":"","copyAsset":false,"role":"figure","size":1038390,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1. \u003c/strong\u003eLegend (350 words max). Example legend text.\u003c/p\u003e\n\u003cp\u003eFigures and tables can be referenced in LaTeX using the ref command, e.g. Figure 1and Table 1.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/e1a564be01e2bd7256106b08.png"},{"id":99309045,"identity":"bbb91d2f-b038-4752-84da-781c4cf408ac","added_by":"auto","created_at":"2025-12-31 16:09:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1375587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig1. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) TIMER analysis of DCTPP1 expression variations between paracancerous and malignant tissues in the pancancer dataset. (\u003cstrong\u003eB\u003c/strong\u003e) DCTPP1 expression in paracancerous and malignant tissues within the TCGA BRCA dataset. (\u003cstrong\u003eC, D\u003c/strong\u003e) Differences in DCTPP1 expression across different stages (C) and subtypes (D) of breast cancer. (\u003cstrong\u003eE\u003c/strong\u003e) DCTPP1 immunohistochemical results in breast cancer tissue (right) and normal breast tissue (left). (\u003cstrong\u003eF, G\u003c/strong\u003e) Kaplan‒Meier curves from the GEPIA database for the DCTPP1 high/low-expression groups. (\u003cstrong\u003eH, I\u003c/strong\u003e) Forest plot of DCTPP1-based OS and DFS predictive correlations across cancers (\u003cstrong\u003eH\u003c/strong\u003e) and breast cancer types (I). (\u003cstrong\u003eJ, K\u003c/strong\u003e) DCTPP1 expression in breast cancer (K) and pancancer (J) samples was correlated with immune cells. (*** P\u0026lt;0.001; OS: overall survival; DFS: disease-free survival)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/3aa6e6137981cc4c93b396d2.png"},{"id":98860913,"identity":"239564e5-1148-40be-84c5-8a4ffe970ef7","added_by":"auto","created_at":"2025-12-23 09:19:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":305830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 2\u003c/strong\u003e.(\u003cstrong\u003eA-D\u003c/strong\u003e) GO and KEGG analyses of DCTPP1 were carried out with GREPIA to identify genes with significant associations (PCC\u0026gt; 0.5). (\u003cstrong\u003eE\u003c/strong\u003e) GSEA of DCTPP1-utilizing genes from the cBioPortal database. (\u003cstrong\u003eF\u003c/strong\u003e) GeneMANIA-predicted DCTPP1-interacting genes. (\u003cstrong\u003eG\u003c/strong\u003e) The protein expression of DCTPP1 was noticeably greater in samples with modified NRF2 signaling pathways. (\u003cstrong\u003eG\u003c/strong\u003e) DCTPP1 protein expression was notably greater in samples with altered NRF2 signaling pathways than in typical breast tissue and non-NRF2-altered samples. (\u003cstrong\u003eH\u003c/strong\u003e) Mutation analysis of DCTPP1 was carried out in BRCA patients. (\u003cstrong\u003eI\u003c/strong\u003e) The mutation status of DCTPP1 was analyzed via data from the TCGA.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/8f72c657430aa5ae831369e9.png"},{"id":99308678,"identity":"893cde9a-d5c0-4260-8105-da8d64a780da","added_by":"auto","created_at":"2025-12-31 16:08:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":448549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 3.\u003c/strong\u003e (\u003cstrong\u003eA-F\u003c/strong\u003e) Camptothecin, cisplatin, 5-fluorouracil, paclitaxel, selumetinib, and cyclophosphamide sensitivity prediction in relation to DCTPP1 expression.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/66964fd2101657b56d7f5a84.png"},{"id":99308711,"identity":"9102505e-d044-4b2c-a58e-a267f8bf4db1","added_by":"auto","created_at":"2025-12-31 16:09:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1143308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 4.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) qRT‒PCR and Western blotting were used to confirm the knockdown efficiency. (\u003cstrong\u003eB\u003c/strong\u003e) MTT assays revealed no statistically significant inhibitory effect on cell proliferation following DCTPP1 knockdown. (\u003cstrong\u003eC\u003c/strong\u003e) Cell adhesion studies revealed decreased MCF-7 adhesion ability after DCTPP1 knockdown. (\u003cstrong\u003eD\u003c/strong\u003e) Following DCTPP1 knockdown, the capacity of MCF-7 cells to migrate was decreased, as shown by the scratch assay. (\u003cstrong\u003eE\u003c/strong\u003e) Transwell assays verified changes in the invasion ability of MCF-7 cells after DCTPP1 knockdown. (\u003cstrong\u003eF\u003c/strong\u003e) E-cadherin and TGF-β expression levels in the blank, si-NC, and si-DCTPP1 groups were determined via qRT‒PCR and Western blotting. All the data were statistically analyzed through 3 mechanical repetition trials via one-way ANOVA. (**P\u0026lt;0.01; ***P\u0026lt;0.001; ****P\u0026lt;0.0001)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/d310aaf9f814b3b6ac6b8715.png"},{"id":98860939,"identity":"dd13397a-ed3a-4e8c-b7bf-dfc05b2ebbdb","added_by":"auto","created_at":"2025-12-23 09:19:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1051845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 5\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) MTT assay confirming the effect of docetaxel on MCF-7 cell proliferation following DCTPP1 knockdown. (\u003cstrong\u003eB\u003c/strong\u003e) Cell adhesion assay to assess the joint impact of DCTPP1 knockdown and docetaxel on MCF-7 cell adhesion. (\u003cstrong\u003eC\u003c/strong\u003e) Wound healing assay to evaluate how si-DCTPP1 and docetaxel regulate MCF-7 cell migration.(\u003cstrong\u003eD\u003c/strong\u003e) Transwell assays revealed significantly reduced invasion capacities . (\u003cstrong\u003eE\u003c/strong\u003e) The mRNA levels of DCTPP1, E-cadherin, and TGF-β were measured via qRT‒PCR and Western blot analysis in groups treated with docetaxel, si-NC combined with docetaxel, and si-DCTPP1 combined with docetaxel. All experiments were performed in triplicate to ensure reliability, and the results from these 3 independent replicates were subjected to statistical analysis. Except for the E-cadherin qRT‒PCR data, which were distorted and examined via the Kruskal‒Wallis rank sum test, the other results were statistically examined via one-way ANOVA. (Significance levels: *P\u0026lt;0.05; **P\u0026lt;0.01; ***P\u0026lt;0.001; ****P\u0026lt;0.0001.)\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/f5a291a3977beed754a635fd.png"},{"id":99308554,"identity":"3d7fa894-a6b0-4615-a9dd-aacda4f2fe33","added_by":"auto","created_at":"2025-12-31 16:08:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":192279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 6\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) MTT was used to assess the effects of DCTPP1 knockdown before and after docetaxel treatment. (\u003cstrong\u003eB\u003c/strong\u003e) Cell adhesion assays were used to evaluate the effect of DCTPP1 knockdown following docetaxel therapy. (\u003cstrong\u003eC\u003c/strong\u003e) Wound healing assays demonstrated the impact of DCTPP1 knockdown on docetaxel-treated cells. (\u003cstrong\u003eD\u003c/strong\u003e) Transwell assays demonstrated the impact of DCTPP1 knockdown on docetaxel-treated cells.(\u003cstrong\u003eE\u003c/strong\u003e)The effects of DCTPP1 knockdown on DCTPP1, E-cadherin, and TGF-β mRNA levels following docetaxel treatment were compared . All the experiments were performed with 3 mechanical replicas. (*P\u0026lt;0.05; **P\u0026lt;0.01; ***P\u0026lt;0.001; ****P\u0026lt;0.0001)\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/8a9ca60d72d7380c8dd3ed8f.png"},{"id":99308642,"identity":"c98f7918-5dab-4ea0-a1de-9653f97009da","added_by":"auto","created_at":"2025-12-31 16:08:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2716136,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 7\u003c/strong\u003e.(\u003cstrong\u003eA\u003c/strong\u003e) Representative imageof subcutaneous tumors in BALB/c mice with measurements of tumor length and variation. (\u003cstrong\u003eB\u003c/strong\u003e) IHC images of DCTPP1, E-cadherin, and TGF-β in xenograft tumors. (\u003cstrong\u003eC\u003c/strong\u003e) Results of the IHC analysis. (*P\u0026lt;0.05; **P\u0026lt;0.01; ***P\u0026lt;0.001; ****P\u0026lt;0.0001)\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/99223f2ec382eff8458db860.png"},{"id":106401817,"identity":"85fbb3d2-b519-4aa7-a4b9-178fb7d1c16a","added_by":"auto","created_at":"2026-04-08 09:09:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9756024,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/14a6b85c-f802-4ba5-bf85-81b7d68cbdd2.pdf"},{"id":98860925,"identity":"2821b4bd-2a83-4994-9e7f-495ecf2e1196","added_by":"auto","created_at":"2025-12-23 09:19:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11972,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1Listofprimersequences.docx","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/2f05854dd971628e12c59bd0.docx"},{"id":98860914,"identity":"ff62c6af-b68c-46d7-b047-912570ed98aa","added_by":"auto","created_at":"2025-12-23 09:19:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11836,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3ListofIHCAntibodyInform.docx","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/d8363b4e7a53ea9072cba406.docx"},{"id":98860923,"identity":"e9d4fe99-5232-4906-98d6-7957c87d8edf","added_by":"auto","created_at":"2025-12-23 09:19:40","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11587,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2ListofWBAntibodyInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/3012bc0c72600203ad045116.docx"},{"id":99308696,"identity":"5b9fc2cb-3461-4997-9e27-06e4d808c777","added_by":"auto","created_at":"2025-12-31 16:08:59","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":948012,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/1bffb6d685f2ba087f721f1f.docx"},{"id":98860930,"identity":"928ba344-4153-4115-acf9-80a1fca91cd0","added_by":"auto","created_at":"2025-12-23 09:19:41","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11972,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1Listofprimersequences.docx","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/22deeb51023c2d2f0eac5bd1.docx"},{"id":98860927,"identity":"ad51bdfe-2743-4b45-9124-43d78b760a89","added_by":"auto","created_at":"2025-12-23 09:19:40","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":11587,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2ListofWBAntibodyInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/cd8bf2e916748d32d2266721.docx"},{"id":99308557,"identity":"dc69b39b-a3dd-4893-9d5c-2e826495db65","added_by":"auto","created_at":"2025-12-31 16:08:46","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":11836,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3ListofIHCAntibodyInform.docx","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/49cb09ab2980e02465f1e4fb.docx"},{"id":99308610,"identity":"b0ac55bf-42a6-41f7-8974-d8841c7c92ac","added_by":"auto","created_at":"2025-12-31 16:08:49","extension":"jpg","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":127434,"visible":true,"origin":"","legend":"","description":"","filename":"bactin.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/d985c2f4cf5cd5149e18ff01.jpg"},{"id":99308646,"identity":"83800ce8-0206-4894-9c21-60f2a420ac7b","added_by":"auto","created_at":"2025-12-31 16:08:53","extension":"jpg","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":146669,"visible":true,"origin":"","legend":"","description":"","filename":"Ecsdherin.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/fc896e7710a331c380a65a66.jpg"},{"id":98860917,"identity":"d0c2027c-fb73-4a0f-9dcc-795c885911f9","added_by":"auto","created_at":"2025-12-23 09:19:40","extension":"jpg","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":129999,"visible":true,"origin":"","legend":"","description":"","filename":"DCTPP1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/8dc08994365da87ede9addb8.jpg"},{"id":99308761,"identity":"d5d11908-999d-43e6-b43d-faa11b831a7b","added_by":"auto","created_at":"2025-12-31 16:09:06","extension":"jpg","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":266420,"visible":true,"origin":"","legend":"","description":"","filename":"TGFb.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/665ecc5582dc169daa008d24.jpg"},{"id":98860935,"identity":"dabcf8fe-1450-4462-a2da-c5aecb750140","added_by":"auto","created_at":"2025-12-23 09:19:41","extension":"zip","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":640351,"visible":true,"origin":"","legend":"","description":"","filename":"WB.zip","url":"https://assets-eu.researchsquare.com/files/rs-8065730/v1/fb6fe278b3b2d1dcccf51768.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pancancer analysis of DCTPP1 and the impact of the combined knockdown of DCTPP1 with docetaxel on breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, breast cancer has an exceptionally high incidence worldwide, accounting for almost one-third of all malignant tumors in women and 15% of all malignant tumors detected in female patients[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Standard treatment protocols for breast cancer include surgery, radiation, chemotherapy, or combination therapies, with chemotherapy serving as the mainstay of systemic therapy[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite improvements in overall survival rates for patients with breast cancer, problems such as tumor recurrence, medication resistance, and distant metastasis still have a major influence on patient outcomes[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The main mechanism of chemotherapy involves DNA damage-induced cell death. Thus, DNA repair pathways remain a critical focus in the exploration of chemotherapy resistance. Given the intricate molecular pathways driving breast cancer development, recurrence, drug resistance, and metastasis, finding novel prognostic biomarkers and therapeutic targets could significantly improve patient outcomes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDCTPP1 is found on chromosome 16 and is encoded by deoxycytidine triphosphate pyrophosphatase 1 (DCTPP1) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This enzyme is a member of the nucleotide triphosphate superfamily [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], specifically functioning as a dCTP hydrolase that removes aberrant dCTP molecules to preserve the stability of the deoxyribonucleotide triphosphate (dNTP) pool. Its hydrolytic action is essential for ensuring precise DNA replication and stable genomic expression. Numerous studies have suggested that DCTPP1 may be a potential target for anticancer medications, with emerging evidence highlighting its major influence on tumor growth, chemotherapy resistance, and prognosis prediction. Consequently, DCTPP1 has become a promising nucleotide metabolism-related target in cancer therapy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, its precise function in breast cancer progression remains unclear.\u003c/p\u003e \u003cp\u003eEMT (epithelial‒mesenchymal transition) is crucial for tumor metastasis and invasion and can even affect resistance to treatment. As a dynamic and intricate process, EMT involves the loss of epithelial cells and the development of mesenchymal cell traits. Tumor metastasis begins with EMT, resulting in distant spread through limited expression of E-cadherin and high expression of cadherins such as N-cadherin [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Treatment strategies are much more difficult in this metastatic phase. EMT transcription factors such as TGF-β, Twist, and ZEB can induce EMT development and increase tumor spread. Studies have shown that EMT is linked to treatment resistance in a number of cancer types. Recent research suggests that EMT-related transcription factors may increase radioresistance and chemotherapy resistance by interfering with DNA repair processes. For example, TGF-β hinders homologous recombination-dependent DNA repair [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], although more research is needed to understand its molecular mechanisms.\u003c/p\u003e \u003cp\u003eThis study revealed that DCTPP1 is highly expressed in BRCA and is positively correlated with EMT pathway activation. As a tumor-promoting gene, its low expression increases the effectiveness of docetaxel in suppressing breast cancer. In vitro experiments demonstrated that DCTPP1 knockdown inhibited MCF-7 cell proliferation, adhesion, metastasis, and invasion. In vivo studies further confirmed that decreased DCTPP1 expression successfully inhibited the growth of breast cancer tumors.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDCTPP1 expression patterns and clinical relevance in breast cancer and pancancer\u003c/h2\u003e \u003cp\u003eThe TIMER and UALCAN datasets were used for pancancer analysis of DCTPP1 expression differences between neighboring and malignant tumor tissues. The results revealed significant upregulation of DCTPP1 in 14 tumor types (BRCA, BLCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRP, LIHC, LUAD, LUSC, STAD, and UCEC), whereas DCTPP1 expression was downregulated in 3 tumor types (KICH, KIRC, and THCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Analysis of TCGA BRCA data revealed significantly greater DCTPP1 expression in malignant tissues than in normal tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). DCTPP1 expression varied across normal tissues and four breast cancer stage groups according to the UALCAN study. (Normal vs. Stage 1\u0026ndash;4: P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Stage 1 vs. Stage 3: P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, DCTPP1 exhibited distinct expression patterns between normal tissues and 3 kinds of breast cancer: the normal and luminal subtypes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), the normal and HER2-positive subtypes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and the normal and TNBC subtypes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Notably, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eD shows a significant difference between the luminal subtype and TNBC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLegend (350 words max). Example legend text.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to imaging data from the Human Protein Atlas, normal breast tissue has modest expression of DCTPP1. However, breast cancer tissue has much higher levels (left: Patient ID#2773; right: Breast cancer tissue, Patient ID#1874). K‒M curves generated via GEPIA were used to analyze the overall survival (OS) and disease-free survival (DFS) of breast cancer patients in the high- and low-DCTPP1 expression groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In pancancer prognostic forest models for DCTPP1, according to the OS forest plot, DCTPP1 acts as a risk factor in 12 tumor types (BRCA, GBMLGG, LGG, TARGET-LAML, SARC, LIHC, SKCM, SKCM-M, TARGET-NB, PAAD, LAML, and TARGET-ALL-R) while protecting against seven different forms of tumors (UCEC, STES, KIPAN, COADREAD, STAD, KIRC, READ). Interestingly, BRCA was associated with OS (HR of 1.67) and DFS (HR of 0.17). The DFS forest plot revealed that DCTPP1 has an adverse effect on prognosis when it is highly expressed in two tumor types (LIHC and PAAD), whereas poor prognosis is associated with low expression of DCTPP1 in STES (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Sangerbox online platform was used to examine the relationship between DCTPP1 expression and immune cell profiles across cancers. DCTPP1 expression and CD4\u0026thinsp;+\u0026thinsp;T cells are negatively correlated in the majority of tumor types. In PCPG, KIRC, LIHC, PRAD, THCA, and PAAD, DCTPP1 exhibited favorable relationships with 3 or more immune cell types. Conversely, in BRCA, LUSC, ESCA, SKCM, and DLBC, it exhibited negative relationships with 3 or more immune cell types. DCTPP1 is negatively correlated with B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and neutrophils in breast cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The TIMER platform confirmed a negative association between DCTPP1 expression and CD8\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, DCTPP1 expression was negatively related to both the stromal score and immune score, in addition to the ESTIMATE score, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional prediction of DCTPP1\u003c/h3\u003e\n\u003cp\u003eTo clarify the DCTPP1-related signaling mechanisms in breast cancer, we performed GO, KEGG and GSEA. These findings indicate that DCTPP1 is strongly involved in chromatin remodeling, DNA repair, and protein ubiquitination. Cellular components are found primarily in the nucleus and cytoplasm, whereas molecular activities include protein binding and metal ion binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The KEGG results revealed that DCTPP1 was enriched in tumor-associated mTOR signaling pathways. GSEA further confirmed that DCTPP1-related genes are positively related to DNA repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Coremine suggested that the DCTPP1 gene may be involved in a number of biological processes and pathways, such as nucleotide metabolism, DNA demethylation, and WNT signaling pathways, which play important roles in tumor development. The DCTPP1 gene may be associated with a number of illnesses, including breast cancer, pre-B-cell lymphoblastic leukemia, and ovarian malignancies (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo elucidate the molecular regulatory mechanisms of DCTPP1, we mined GeneMINA for its interaction links and identified 11 interacting genes. Interestingly, DCTPP1 exhibited stronger physical interactions with CIAPIN1 and NUBP (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Mapping PPIs from the STRING database with Cytoscape revealed 10 interacting proteins that cover DNA synthesis/repair (RRM1, RRM2), nucleotide metabolism (AK9, DCTD), deoxyribonucleotide salvage pathway (DCK), and cell cycle regulation (NME1, NME2) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In contrast to that in normal breast tissue and non-NRF2-altered samples, DCTPP1 expression was significantly greater in samples with altered NRF2 signaling (normal vs. NRF2-altered samples, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; normal vs. NRF2-altered samples, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Furthermore, DCTPP1 was highly expressed in specimens whose RTK pathways, p53/Rb pathways, WNT pathways, and mTOR pathways were altered, all of which were significantly greater than those in normal breast tissue and pathway-altered samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo investigate the mutational features of DCTPP1, we examined its mutation and expression patterns in Sangerbox. Significant changes were found in 15 tumor types: BRCA, CESC, LUAD, COAD, ESCA, STES, SARC, KIPAN, STAD, PRAD, HNSC, LUSC, LIHC, OV, and BLCA, with BRCA primarily showing gain-of-function (gain-of-function) mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). By excluding samples with wild-type mutations from the BRCA cBioPortal database, we identified 51 breast cancer mutation cases, revealing both frameshift mutations and multiple-frame mutations in DCTPP1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDCTPP1 and chemotherapeutic drug sensitivity prediction\u003c/h3\u003e\n\u003cp\u003eAnalysis of DCTPP1 expression and its correlation with chemotherapeutic sensitivity in breast cancer revealed no statistically significant associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The correlations, although statistically insignificant, were weakly negative for camptothecin (n\u0026thinsp;=\u0026thinsp;2 570, R=-0.019) and 5-fluorouracil (n\u0026thinsp;=\u0026thinsp;1 875, R=-0.012) and weakly positive for selumetinib (n\u0026thinsp;=\u0026thinsp;3 452, R\u0026thinsp;=\u0026thinsp;0.03). No correlation was detected for cyclophosphamide (n\u0026thinsp;=\u0026thinsp;957, R=-0.003), paclitaxel (n\u0026thinsp;=\u0026thinsp;1 353, R=-0.008), or cisplatin (n\u0026thinsp;=\u0026thinsp;2613, R=-0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.4 \u003cb\u003eCellular level confirmation that DCTPP1 promotes the proliferation, migration and invasion of breast cancer cells\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDCTPP1 was knocked down in the MCF-7 cell line. Successful DCTPP1 knockdown in MCF-7 cells was verified by qRT‒PCR and Western blotting (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The MTT assay results demonstrated that DCTPP1 did not affect breast cancer cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Cell adhesion experiments revealed a significantly reduced adhesion capacity of breast cancer cells following DCTPP1 knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Wound healing and Transwell assays revealed that si-DCTPP1 dramatically reduced cell migration and invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Previous studies have established the crucial role of EMT in tumor metastasis. We postulated that DCTPP may affect breast cancer cell migration and invasion through EMT. qRT‒PCR and Western blot analyses of E-cadherin and TGF-β expression revealed increased E-cadherin levels and decreased TGF-β expression in DCTPP-deficient cells. These results support a favorable connection between DCTPP1 and EMT (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAt the cellular level, inhibition of DCTPP1 expression can enhance the inhibitory effect of docetaxel on breast cancer\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo assess the clinical effectiveness of DCTPP1 in breast cancer treatment, 3 experimental groups were created: the docetaxel treatment group, the si-NC combined with docetaxel treatment group, and the si-DCTPP1 combined with docetaxel treatment group. MTT assays revealed that the OD values in the docetaxel-knockdown group were substantially lower than those in the docetaxel-only group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Cell adhesion assays revealed a marked decrease in cell adhesion capacity in the docetaxel\u0026thinsp;+\u0026thinsp;knockdown group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Wound healing and Transwell assays revealed significantly reduced migratory and invasion capacities in these groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). To examine whether DCTPP1 decreases the effectiveness of docetaxel through EMT, we used qRT‒PCR and Western blotting to measure the expression of DCTPP1, E-cadherin, and TGF-β. The results revealed that the DCTPP1 and TGF-β expression levels in the docetaxel\u0026thinsp;+\u0026thinsp;DCTPP1 group were dramatically lower than those in the other two groups, whereas E-cadherin expression tended to increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study performed a comprehensive analysis of six experimental groups to investigate the connection between the tumor-suppressive effects of DCTPP1 and docetaxel. The MTT analysis demonstrated that the OD values in the si-DCTPP1 plus docetaxel group were dramatically lower than those in the Control, si-NC, and si-DCTPP1 groups, with noticeable reductions in the si-DCTPP1 plus docetaxel groups compared with the single-drug docetaxel group compared with the Control group or docetaxel in combination with the si-NC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The cell adhesion assay revealed that the OD values in the si-DCTPP1 plus docetaxel group were also significantly lower than those in the control, si-NC, and si-DCTPP1 groups, with more noticeable reductions than those in the single-drug docetaxel group versus the control group or docetaxel in combination with the si-NC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Both the cell scratch assay and Transwell assay indicated that the si-DCTPP1 plus docetaxel group demonstrated significantly lower scratch healing percentages than the control and si-NC groups did, with more noticeable decreases than those in the single-drug docetaxel group compared with the control group or the si-NC group combined with docetaxel. However, the scratch healing percentage of the si-DCTPP1 group did not differ significantly from that of the si-DCTPP1 plus docetaxel group. These results suggest that docetaxel may exert its effects by lowering DCTPP1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). DCTPP1, E-cadherin, and TGF-β expression was markedly lower in the si-DCTPP1 plus docetaxel group than in the control and si-NC groups, according to qRT‒PCR analysis. Notably, DCTPP1 expression was lower in the docetaxel group than in the control group, while changes in E-cadherin and TGF-β levels were not statistically significant. The si-NC group demonstrated comparable outcomes to those of the si-NC plus docetaxel group. The expression of DCTPP1, E-cadherin, and TGF-β did not differ significantly between the si-DCTPP1 group and the knockdown plus docetaxel group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eInhibition of DCTPP1 expression can inhibit breast cancer proliferation in vivo\u003c/h3\u003e\n\u003cp\u003eOn the basis of the inhibitory effect of DCTPP1 on MCF-7 cell proliferation in vitro, we created a mouse model to validate its in vivo anti-breast cancer effectiveness. The findings demonstrated that tumors in mice transplanted with low-DCTPP1-expressing cells presented significantly decreased weight and shortened length (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). To verify pertinent gene expression levels, immunohistochemical staining was performed on tumor sections (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). E-cadherin expression was not significantly altered in DCTPP1-low-expressing animals. The TGF-β levels tended to decrease but lacked statistical significance. In the si-DCTPP1 plus docetaxel group, both E-cadherin and TGF-β levels tended to decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBreast cancer has alwas been the primary source of disease burden among women. Low survival rates, high recurrence rates, and an adverse prognosis are common characteristics of advanced breast cancer. because of medication resistance [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. When breast cancer spreads to surrounding tissues and lymph nodes, treatment efficacy is severely impaired. EMT (epithelial‒mesenchymal transition) is known to be a critical mechanism causing cancer metastasis and chemotherapy resistance, accelerating disease progression [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Notably, EMT and DNA damage are regulated in both directions: DNA damage triggers EMT, while EMT transcription factors (EMT-TFs) promote DNA repair through pathways that are regulated in both directions: the EGFR-Erk1/2/Akt-Rad51 axis [\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. DCTPP1 preserves dNTP pool homeostasis by hydrolyzing dCTP and its derivatives (e.g., 5-methyldCTP), preventing nucleotide metabolic imbalances that result in mistakes in DNA replication and genomic instability [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. DCTPP1 influences numerous aspects of cancer progression, prognosis, and treatment results. For example, targeting DCTPP1 in colorectal cancer disrupts amino acid reprogramming for tumor suppression [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], enhances gastric cancer cell proliferation with prognostic correlations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and reverses cisplatin-induced resistance in ovarian cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These results direct our research into whether DCTPP1 drives breast cancer malignancy by enhancing the EMT process.\u003c/p\u003e \u003cp\u003eThis study conducted a systematic bioinformatics investigation of DCTPP1 in multiple cancer and breast cancer subgroups, revealing its possible roles in tumor growth and immune microenvironment regulation. Previous research has indicated that DCTPP1 acts as a prognostic biomarker across cancers, is negatively correlated with sensitivity to different chemotherapeutic agents, and affects tumor growth by modifying the cell cycle and immune system [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our research revealed that DCTPP1 is significantly upregulated in 14 types of tumors but downregulated in 3 types, with high DCTPP1 expression strongly linked to poor prognosis in breast cancer patients. This finding implies that DCTPP1 may serve as a significant prognostic factor at the pancancer level, which is consistent with previous findings [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, high DCTPP1 expression is strongly correlated with immune cell infiltration across a variety of tumor types. Further exploration of its expression and function in breast cancer was performed. Previous reports indicate that DCTPP1 is extensively overexpressed in breast cancer (particularly the luminal A subtype), with copy number amplification being the primary mutation type, and its high expression is strongly associated with poor prognosis and treatment resistance [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our team further demonstrated that DCTPP1 expression increases dramatically in different stages and molecular subtypes of breast cancer (Luminal, HER2+, TNBC). Subsequent mutation spectrum analysis revealed that after wild-type samples were removed, DCTPP1 mutations were primarily missense and multiallelic mutations. Misense mutations (e.g., HER2 missense mutations) have been shown to increase resistance to specific treatments in breast cancer cells [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. High expression of DCTPP1 was negatively correlated with the breast cancer stromal score, immunoscore, and ESTIMATE score, in addition to being negatively correlated with CD8\u0026thinsp;+\u0026thinsp;T-cell infiltration, neutrophil, B cell, and CD4\u0026thinsp;+\u0026thinsp;T-cell infiltration. These findings suggest that DCTPP1 may inhibit immune cell infiltration and microenvironment activity, thereby facilitating immune evasion in breast cancer cells. Although the relationship between DCTPP1 expression and chemotherapeutic drug sensitivity was not statistically significant, it still exhibited weak negative correlations with camptothecin and 5-fluorouracil and weak positive associations with selumetinib.\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis and molecular interaction network studies explored the possible pathways associated with DCTPP1 in breast cancer. GO and KEGG analyses revealed that DCTPP1 is involved in chromatin remodeling, DNA repair, and protein ubiquitination processes. GSEA also revealed that highly enriched DCTPP1-associated genes were significantly enriched in DNA repair pathways. GeneMINA and STRING database analyses revealed that DCTPP1 forms interaction networks with several DNA repair genes (e.g., RRM1 and RRM2) and that high expression of DNA repair genes (e.g., FANCF) promotes breast cancer cell growth [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, the UALCAN platform indicated that DCTPP1 might stimulate tumor development cooperatively through key pathways associated with EMT, such as NRF2, RTK, WNT, p53/Rb, and mTOR. For example, NRF2 controls EMT by inhibiting Akt/GSK3β and maintaining redox equilibrium. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]; RTK (e.g., EGFR) indirectly controls EMT via the PI3K/Akt/mTOR pathway; WNT uses β-catenin to directly activate EMT [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]; p53/Rb participates in EMT through cell cycle and apoptosis regulation; and mTOR influences EMT via the PPARγ-NRF2 and Wnt/PCP pathways [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Together, these five mechanisms work in concert to control EMT in breast cancer cells [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDCTPP1 is essential for preserving DNA replication accuracy and genomic stability [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Mounting evidence indicates that aberrant expression or mutation of DNA repair genes can control EMT-associated genes (e.g., TWIST1 and SNAIL1), thereby promoting breast cancer cell metastasis. For example, DNMT3A activates DNA demethylation to activate the transcription of TWIST1 and SNAIL1 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. CBX3 uses the ERK1/2 signaling pathway to control the expression of genes linked to EMT. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. On the basis of the regulatory antecedents involving the DNMT3A and CBX3 proteins, we speculate that DCTPP1 may regulate EMT in breast cancer and increase its metastatic invasiveness. Our data revealed that DCTPP1 knockdown decreases the growth of breast cancer cells, although the difference was not statistically significant. This decreasing trend is consistent with previous experimental findings, but earlier analyses used time series line plots without statistical examination [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The experimental results revealed that DCTPP1 knockdown decreases the cell adhesion capacity and profoundly reduces both invasion and metastasis. qRT‒PCR, Western blotting, and immunohistochemistry all verified that DCTPP1 knockdown increased E-cadherin expression while suppressing TGF-β. These results suggest that DCTPP1 knockdown can inhibit EMT to weaken the malignant biological activity of breast cancer.\u003c/p\u003e \u003cp\u003eDocetaxel and paclitaxel belong to the taxane family, which stabilizes tubulin and causes tumor cells to enter the cell cycle. Docetaxel plays a prominent role in breast cancer treatment [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]; however, tumor cells activate EMT, increasing their resistance to docetaxel [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. According to recent research, docetaxel resistance can be reversed by focusing on EMT. For example, recombinant methionine synthetase (rMETase) dramatically increases docetaxel's effectiveness against drug-resistant osteosarcoma and soft tissue sarcoma cells by inhibiting EMT-related mechanisms [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In esophageal squamous cell carcinoma, downregulation of NOTCH3 activates the EMT marker vimentin (VIM), resulting in treatment resistance to medications such as cisplatin [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. CTEN upregulates TGF-β1 expression through demethylation, promoting EMT and increasing chemotherapy resistance in breast cancer (BC) cells [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Furthermore, DNA repair genes are reported to play important roles in chemotherapy resistance. KMT2C-mediated suppression of DNA damage repair-related genes enhances breast cancer sensitivity to chemotherapy following its knockdown [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. DCTPP1 affects cisplatin resistance in ovarian cancer through PI3K/Akt signaling [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], although its impact on docetaxel resistance in breast cancer remains unknown. Our research team revealed for the first time that DCTPP1 expression decreases following docetaxel treatment. Subsequent studies revealed that coadministering docetaxel with DCTPP1 knockdown considerably increased E-cadherin expression and decreased TGF-β levels, with the most pronounced effects observed in the combination treatment group. Functionally, this combination strategy resulted in the most significant reduction in breast cancer proliferation, adhesion, migration, and invasion among all six groups, demonstrating significantly superior efficacy compared with the other treatments. Animal research revealed that tumors in mice implanted with DCTPP1-low-expressing MCF-7 cells presented marked weight reduction and shortened tumor length. Immunohistochemical examination revealed that DCTPP1-only knockdown mice presented no significant changes in E-cadherin expression, with the exception of a statistically negligible decrease in TGF-β. However, the E-cadherin and TGF-β levels decreased in the si-DCTPP1 and docetaxel cotreatment groups. The lack of statistically significant variations may reflect complex intracellular processes. Collectively, these findings suggest that DCTPP1 knockdown could reverse the EMT process in breast cancer cells, thereby increasing docetaxel sensitivity and synergistically decreasing malignant biological behaviors.\u003c/p\u003e \u003cp\u003eThis study performed comprehensive bioinformatics analysis and validated the findings through \"in vitro-in vivo\" multilevel experiments, elucidating the novel mechanism by which DCTPP1 drives EMT to increase breast cancer progression and docetaxel resistance. These results offer novel treatment targets and approaches to overcome chemotherapy resistance in breast cancer.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eGenetic expression data\u003c/h2\u003e \u003cp\u003eClinical information and gene expression profiles of patients with breast cancer from the TCGA database (normal samples, 113; cancer samples, 1,109) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003ePancancer analysis\u003c/h3\u003e\n\u003cp\u003eUsing the TIMER (\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) and Sangerbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sangerbox.com/\u003c/span\u003e\u003cspan address=\"http://sangerbox.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) online platforms, we examined the variations in DCTPP1 expression between tumor tissues and normal tissues in a range of malignancies. The OS and DFS forest plots of DCTPP1 expression and patient prognosis were created via Sangerbox.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of DCTPP1 expression in breast cancer\u003c/h2\u003e \u003cp\u003eWe analyzed the differences in the expression of DCTPP1 in breast cancer via R (4.4.1) and GEPIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Human Protein Atlas (\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) was utilized to obtain immunohistochemistry images of DCTPP1 in both normal breast tissue and breast cancer tissues. TIMER was used to analyze immune infiltration, whereas GEPIA was used to analyze OS and disease-free survival (DFS) with the Kaplan‒Meier method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFunctional exploration of DCTPP1\u003c/h2\u003e \u003cp\u003eWe first investigated the pathways enriched with genes linked to DCTPP1. We determined the top 200 breast cancer-associated genes associated with DCTPP1 in GEPIA and identified genes with a PCC\u0026thinsp;\u0026ge;\u0026thinsp;0.3 from UALCAN, resulting in the identification of a total of 473 DCTPP1-related genes. DAVID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we acquired information from Gene Ontology (GO) and functional enrichment analysis (KEGG) data for these genes. Microarray analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.microbioinfo.com/\u003c/span\u003e\u003cspan address=\"https://www.microbioinfo.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was performed to screen genes with counts\u0026thinsp;\u0026gt;\u0026thinsp;5 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Using R, we collected TCGA-BRCA data that demonstrated correlations\u0026thinsp;\u0026gt;\u0026thinsp;0.3 between DCTPP1-related genes and breast cancer, along with pathway data from GSEA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Finally, GSEA maps showing the involvement of DCTPP1 in breast cancer were created via GSEA software.\u003c/p\u003e \u003cp\u003eSecond, we built the interaction network of DCTPP1. GeneMAN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genemina.org/\u003c/span\u003e\u003cspan address=\"https://www.genemina.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to construct the gene network of DCTPP1. From STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we mapped the PPI network of DCTPP1 via Cytoscape 3.10.3 after the protein interaction data were downloaded.\u003c/p\u003e \u003cp\u003eFinally, Sangerbox performed a pancancer gene mutation analysis of DCTPP1. cBioPortal uses R to create a mutation waterfall graph after the gene mutation data for DCTPP1 in breast cancer are downloaded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of the effect of DCTPP1 on drug sensitivity in breast cancer\u003c/h2\u003e \u003cp\u003eThe GDSC drug sensitivity data and TCGA clinical data were downloaded. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Box plots and drug sensitivity correlation diagrams were drawn via R (4.4.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and DCTPP1 knockdown\u003c/h2\u003e \u003cp\u003eMCF-7 cells were purchased from Procell Life Science \u0026amp; Technology Co., Ltd. MCF-7 cells were cultivated in DMEM supplemented with 1% penicillin/streptomycin and 10% fetal bovine serum (FBS). The cells were kept in a cell culture incubator at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eTo create a stable transfection line with low DCTPP1 expression, MCF-7 cells were transfected with the pLVX-ShRNA2-Puro plasmid expression vector. The matching empty vector group served as a control. The supplier of the pLVX-ShRNA2-Puro-Homo-DCTPP1-479 plasmid was Wuhan Bafei Biotechnology Service Co., Ltd. For temporary transfection, 10 \u0026micro;L of viral mixture was applied to each well according to the manufacturer's instructions. Following a 24 h incubation period at 37\u0026deg;C, fresh media was added. At 48 h post infection, the medium was replaced with medium containing 2 \u0026micro;g/mL puromycin, and then, the medium was replaced with new medium containing 2 \u0026micro;g/mL puromycin every 2\u0026ndash;3 days, resulting in many dead cells. This process was repeated until the antibiotic-resistant colonies were identified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eReal-time fluorescence quantitative PCR (qRT‒PCR)\u003c/h2\u003e \u003cp\u003eAfter TRIzol reagent was used to lyse the sample cells, 200 \u0026micro;L of chloroform was added. After mixing and incubation at room temperature for 5 min, the layers were separated by centrifuging the mixture. The RNA-containing aqueous phase (approximately 400 \u0026micro;L) was combined with 400 \u0026micro;L of isopropanol for RNA precipitation. Following a 75% ethanol wash, the mixture was centrifuged, dried, and dissolved in 20 \u0026micro;L of DEPC buffer. The RNA purity and concentration were calculated by measuring the OD260, OD280, and OD260/OD280 values of a 2 \u0026micro;L sample of the RNA mixture via a microplate reader. Reverting was carried out via HiScript\u0026reg; II Q RT SuperMix for qRT‒PCR (with gDNA wiper) on the basis of the determined RNA concentration, followed by qRT‒PCR with VAZYME SYBR Green Master Mix. The 2\u003csup\u003e-△△Ct\u003c/sup\u003e method was used to examine the experimental results. The experimental results were analyzed via the 2\u003csup\u003e-△△Ct\u003c/sup\u003e method. Supplementary Table\u0026nbsp;1 contains a list of the qRT‒PCR primers used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot\u003c/h2\u003e \u003cp\u003eOne milliliter of RIPA buffer was prepared, which contained 10 \u0026micro;L of 100 mM PMSF and 10 \u0026micro;L of phosphatase inhibitor. Following the completion of cell growth in six-well plates, 120 \u0026micro;L of PMSF-containing lysis buffer was added to each well, and the mixture was subjected to ice-cold lysis for 30 minutes. After the cell lysate was collected, the sample was diluted ten times. The protein concentration was determined via a BCA protein assay kit (Beyotime). The cell lysate was then combined with 5\u0026times; protein loading buffer at a 4:1 volume ratio and cooked at 100\u0026deg;C for 10 minutes to denature the proteins. The mixture was subjected to SDS‒PAGE, followed by transfer to a PVDF membrane. The membrane was incubated with 5% skim milk at room temperature for 2 h to prevent nonspecific binding. Primary antibodies (β-actin, TGF-β, DCTPP1, and E-cadherin, diluted 1:1,000) were incubated with the samples overnight at 4\u0026deg;C. The following day, secondary antibodies (HRP-labeled, 1:10,000 dilution) were applied, and the immunoblot signals were visualized via the enhanced chemiluminescence (ECL) method. Supplementary Table\u0026nbsp;2 contains comprehensive details regarding the antibodies used in this experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMTT assay\u003c/h2\u003e \u003cp\u003eMCF-7 cells were plated at 5\u0026times;10\u003csup\u003e3\u003c/sup\u003e cells per well in a 96-well plate. Following cell attachment, the medium was removed according to the manufacturer\u0026rsquo;s instructions and replaced with MTT reagent. The plate was incubated in an incubator for 4 h. After the medium was aspirated, 150 \u0026micro;L of DMSO was added, and the mixture was shaken for 10 min. Finally, a microplate reader (Molecular Devices) was used to measure the absorbance at OD568.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCell adhesion assay\u003c/h2\u003e \u003cp\u003eThey applied a 100 \u0026micro;g/mL fibrinonectin dilution to a 96-well plate. Each well received 100 \u0026micro;L of the MCF-7 cell suspension at 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e/mL. After 2 min of low-speed centrifugation to allow for cell sedimentation, the plates were incubated for 30 min at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e. Aspiration of the detached cells was followed by two washes with PBS. A control group containing normal cells (without cell removal) was created. One hundred microlitres of culture material was added to each well, and images were taken at 20\u0026times; magnification. The absorbance was then measured via the MTT technique.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eWound healing assay\u003c/h2\u003e \u003cp\u003eSix-well plates were inoculated with approximately 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells (digested and counted). The cells were cultivated overnight at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e saturation with humidification. Using a handle tip against a ruler, a scratch was made as close as possible to the back crossbar. After the scratched areas were removed and the cells were washed 3 times with PBS, serum-free media was added. At 0 h, pictures were taken at 10x magnification. The plates were then cultivated in an incubator set at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e. Images were taken again at 10x magnification after a 24 h period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTranswell assay\u003c/h2\u003e \u003cp\u003eMCF-7 cells were diluted to 3\u0026times;10\u003csup\u003e5\u003c/sup\u003e/mL in DMEM. A 24-well plate was filled with 800 \u0026micro;L of 10% FBS DMEM (including biotin) that had been prechilled to 4\u0026deg;C. Transwell chambers were inserted. At the middle of the bottom surface of the upper chamber, 100 \u0026micro;L of Matrigel with a final concentration of 1 mg/mL was added vertically. For four to five h, the mixture was incubated at 37\u0026deg;C until gelatinization occurred. Once gelatinized, 200 \u0026micro;L of each cell suspension (3\u0026times;10\u003csup\u003e5\u003c/sup\u003e/mL) was transferred into the transwell top chamber. For 24 h, the mixture was cultivated at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e. The transwell was removed, the chamber was gently washed with PBS, and the cells were fixed with 70% ice-cold ethanol for 1 h. Sterile cotton balls were used to remove any unmigrated cells from the top chamber side after they were stained with 0.5% crystal violet solution and incubated for 20 min at room temperature. The cells were rinsed with PBS. The samples were examined and imaged using a Nikon inverted microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMice and tumor inoculation\u003c/h2\u003e \u003cp\u003e In compliance with ethical standards, twelve female BALB/c mice aged 5\u0026ndash;6 weeks were acquired fromSpeFud (Beijing) Biotechnology Co., Ltd. and kept at the Experimental Animal Center of the First Affiliated Hospital of Xinjiang Medical University. The researchers anesthetized the mice with 5% sodium pentobarbital at a dose of 55 mg/kg via intraperitoneal injection.The DCTPP1 interference agent was administered subcutaneously to both control and transformed cells. (0.2 mL, 5\u0026times;10\u003csup\u003e6\u003c/sup\u003e) were given under the left flank of the BALB/c mice. After tumor modeling, the mice were randomly split into four groups, with three in each group: normal cells, normal cells\u0026thinsp;+\u0026thinsp;docetaxel, si-DCTPP1, and si-DCTPP1\u0026thinsp;+\u0026thinsp;docetaxel. The normal cells\u0026thinsp;+\u0026thinsp;docetaxel and si-DCTPP1\u0026thinsp;+\u0026thinsp;docetaxel groups were administered 5 mg/kg docetaxel intraperitoneally. The normal cells and si-DCTPP1 groups were administered 0.2 mL of saline solution via intraperitoneal injection once daily for five consecutive 2 d. After that, the tumors were removed, captured on a camera, weighed, and kept as specimens embedded in paraffin. Finally,use the right hand tograsp the base of the mice\u0026rsquo;s tail and lift it, place it firmly on the operating table, fix its head/neck with the left thumb and index finger, and then pull the tail quickly backward and upward to achieve instantaneous cervical dislocation, resulting in immediate unconsciousness.Statistical analysis was performed on tumor portions, and slices of the tumors fixed in paraffin were subjected to immunohistochemical staining.All animal experiments were conducted in accordance with the ARRIVE 2.0guidelines and have been approved by the relevant animal ethics review committee.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eThe tissue slices were subjected to heat-induced antigen retrieval via sodium citrate buffer (pH\u0026thinsp;=\u0026thinsp;6.0) at 98\u0026ndash;100\u0026deg;C. The slides were allowed to cool naturally before being gently agitated on a decolorization shaker for 3 cycles of five-minute rinses in PBS (pH\u0026thinsp;=\u0026thinsp;7.4). After they had dried somewhat, the serum was applied to the slides and incubated at 37\u0026deg;C in a humid box for 30 min. PBS (pH\u0026thinsp;=\u0026thinsp;7.4) was used 3 times to rinse the slides for 3 min each time. Primary antibodies (DCTPP1: 1:20,000 dilution; E-cadherin: 1:100 dilution; TGF-β: 1:500 dilution) were added, and the samples were incubated in a humid box overnight. After the slides were rinsed with PBS, they were rewarmed and coated with MaxVision secondary antibody. The slides were incubated in a humid box at room temperature for 20\u0026ndash;30 min, followed by rinsing with PBS. DAB staining was completed, and when coloration was observed, the stain was quickly removed with tap water. After 3 min of hematoxylin staining, 1% hydrochloric acid alcohol was used to dehydrate the samples. Microscopic inspection was performed to assess the staining intensity. The slides were rinsed with tap water for 10 min. For 5 min each, the samples were submerged in 75%, 85%, 95%, 100%, and 100% alcohol I and II. The slides were then processed in xylene for 3 min twice, followed by mounting with neutral glue. The antibodies used for IHC are listed in Supplementary Table\u0026nbsp;3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis and visualization were performed via R (4.4.1) and GraphPad Prism (10.1.2). All experimental data were analyzed via GraphPad Prism (10.1.2). For normally distributed quantitative data, the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations were calculated via one-way ANOVA for multigroup analysis and independent samples t tests for pairwise comparisons. SNK tests were employed for post hoc comparisons when significant differences were found. Mann‒Whitney U tests for pairwise comparisons, nonparametric Kruskal‒Wallis tests for multigroup comparisons, and median values (quartile ranges) were used to assess nonnormal distributions. Categorical data are expressed as frequencies, rates, or proportions, with χ\u0026sup2; tests applied. Every experiment was carried out 3 times. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistically significant results. We created drug sensitivity analyses, gene mutation waterfall diagrams, and expression variation maps of DCTPP1 in breast cancer using R. Gene mutation frequencies were calculated as frequency = (number of samples with specific mutations/total samples) \u0026times;100%. Regular expressions were used to categorize the different types of mutations. T test comparisons were performed between the high-expressing and low-expressing groups regarding drug sensitivity measures (IC50, AUC). Cohen's d effect size was calculated to measure intergroup significance, with thresholds: \u0026lt;0.2 for small effects, 0.2\u0026ndash;0.5 for medium effects, and \u0026gt;\u0026thinsp;0.5 for large effects. Multiple testing correction (Benjamini‒Hochberg method) was performed to control false positive rates in multiple drug tests, and p-adjust was used to calculate the corrected P value.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eLaTeX formats citations and references automatically using the bibliography records in your .bib file, which you can edit via the project menu. Use the cite command for an inline citation, e.g.2.\u003c/p\u003e\n\u003cp\u003eFor data citations of datasets uploaded to e.g.\u0026nbsp;\u003cem\u003efigshare\u003c/em\u003e, please use the\u0026nbsp;howpublished\u0026nbsp;option in the\u0026nbsp;bib entry to specify the platform and the link, as in the\u0026nbsp;Hao:gidmaps:2014\u0026nbsp;example in the sample bibliography file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements (n\u003c/strong\u003e\u003cstrong\u003eot compulsory)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcknowledgements should be brief, and should not include thanks to anonymous referees\u0026nbsp;and\u0026nbsp;editors,\u0026nbsp;or\u0026nbsp;effusive\u0026nbsp;comments. Grant or contribution numbers may\u0026nbsp;be acknowledged.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions statem\u003c/strong\u003e\u003cstrong\u003eent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMust\u0026nbsp;include\u0026nbsp;all\u0026nbsp;authors,\u0026nbsp;identified\u0026nbsp;by\u0026nbsp;initials,\u0026nbsp;for\u0026nbsp;example:\u0026nbsp;A.A.\u0026nbsp;conceived\u0026nbsp;the\u0026nbsp;experiment(s), A.A.\u0026nbsp;and\u0026nbsp;B.A.\u0026nbsp;conducted\u0026nbsp;the experiment(s), C.A.\u0026nbsp;and\u0026nbsp;D.A.\u0026nbsp;analysed\u0026nbsp;the\u0026nbsp;results.\u0026nbsp;All\u0026nbsp;authors\u0026nbsp;reviewed\u0026nbsp;the\u0026nbsp;manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo include, in this order: \u003cstrong\u003eAccession codes\u0026nbsp;\u003c/strong\u003e(where applicable);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e(mandatory\u0026nbsp;statement).\u003c/p\u003e\n\u003cp\u003eThe corresponding author is responsible for submitting a competing interests statementon behalf of all authors of the paper.\u003c/p\u003e\n\u003cp\u003eThis statement must be included in the submitted article file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlease note\u003c/strong\u003e: Abbreviations should be introduced at the first mention in the main text \u0026ndash; no abbreviations lists. \u0026nbsp;Suggested structure of main text (not enforced) is provided below.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXiong, X. et al. 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KMT2C is a potential biomarker of prognosis and chemotherapy sensitivity in breast cancer. \u003cem\u003eJ. Breast cancer Res. Treat.\u003c/em\u003e \u003cb\u003e189\u003c/b\u003e, 347\u0026ndash;361 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y. et al. Expression of human dCTP pyrophosphatase 1 (DCTPP1) and its association with cisplatin resistance characteristics in ovarian cancer. \u003cem\u003eJ. Cell. Mol. Med.\u003c/em\u003e 28e18371. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e,https://doi.org/10.1111/jcmm.18371\u003c/span\u003e\u003cspan address=\",10.1111/jcmm.18371\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8065730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8065730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e: Examine the carcinogenic function of DCTPP1 in breast cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A pancancer study revealed that high DCTPP1 expression was associated with poor prognosis. In MCF-7 cells, DCTPP1 knockdown (si-DCTPP1) was assessed via MTT, qRT‒PCR, Western blot, adhesion, scratch, and Transwell assays. In vivo tumorigenesis was used to measure tumor growth in vivo. DCTPP1 and EMT markers were detected via immunohistochemistry.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: DCTPP1 was elevated in all tumor types and was linked to negative outcomes. In breast cancer, high DCTPP1 expression was associated with poor prognosis and possible immune evasion, as determined via bioinformatics analysis. si-DCTPP1 suppressed invasion, migration, and proliferation by increasing E-cadherin, decreasing TGF-β, and inhibiting cell adhesion. The combination of si-DCTPP1 and docetaxel synergistically intensified these effects. In vivo, si-DCTPP1 decreased tumor weight and growth. si-DCTPP1 synergistically reduced E-cadherin and TGF-β expression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: DCTPP1 stimulates malignant characteristics in breast cancer. Its knockdown enhances docetaxel efficacy, indicating that DCTPP1 is a possible therapeutic biomarker.\u003c/p\u003e","manuscriptTitle":"Pancancer analysis of DCTPP1 and the impact of the combined knockdown of DCTPP1 with docetaxel on breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-23 09:19:35","doi":"10.21203/rs.3.rs-8065730/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f10f717-75f3-48a3-8e73-6ba691b58082","owner":[],"postedDate":"December 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60034503,"name":"Health sciences/Biomarkers"},{"id":60034504,"name":"Biological sciences/Cancer"},{"id":60034505,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-04-02T01:39:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-23 09:19:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8065730","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8065730","identity":"rs-8065730","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}