TACC3, correlated with immune cell infiltrates, is a novel prognostic biomarker in BRCA.

1. Introduction

Breast cancer (BRCA) is one of the most common invasive malignancies and the 2nd leading cause of cancer-related deaths among females worldwide, posing a significant burden on society. Premised on the statistics from Global Cancer Estimates 2020, [ 1 ] BRCA has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.261 million new cases, accounting for 11.7% of all malignant neoplasms globally. Additionally, approximately 0.685 million new deaths (6.9% of all sites) occur in this population, 2nd only to lung cancer. Given the rising morbidity and mortality rates associated with BRCA, early prevention and treatment have remained critical areas of focus in medical research over the past decade. Fortunately, the widespread promotion of screening tests has led to an increasing number of BRCA patients being diagnosed at earlier stages, allowing them to benefit from timely interventions. Comprehensive treatment strategies encompass multifaceted approaches that integrate both local and systemic therapies, including surgical resection, hormone therapy, (neo)adjuvant chemotherapy and radiation therapy, tailored to individual needs, human epidermal growth factor receptor-2 (HER-2)-targeted therapy for precision medicine, and immunotherapy, resulting in greatly ameliorated survival times and quality of life for patients. Nonetheless, surgery remains the only radical treatment option, and a large number of BRCA patients are diagnosed at an advanced stage, often missing the opportunity for radical resection due to local or systemic aggressiveness. It has been noted that the 5-year survival rate for patients with advanced metastatic BRCA does not exceed 30%, [ 2 ] even with (neo)adjuvant therapies. BRCA has been elucidated to encompass a diverse spectrum of diseases, characterized by distinct presentations, morphologies, biological behaviors, clinical outcomes, and responses to therapy. This variability reflects differences in genetic alternations and diversity, even within individual subtypes. [ 3 – 5 ] Moreover, tumor heterogeneity is exacerbated during tumor progression as the cellular and noncellular components of the tumor microenvironment (TME) mature, presenting a pivotal issue that remains to be addressed. Therefore, it is both urgent and essential to conduct further investigations into the underlying molecular mechanisms and to identify novel and reliable targets for intervention. This research is critical for preventing the progression of malignancy and ultimately aims to develop innovative treatment strategies that improve survival rates for BRCA patients. [ 6 – 10 ]

As an alternative, immunotherapy has emerged as a clinically validated treatment approach that offers significant antitumor benefits across various human cancers, such as melanoma, non-small cell lung cancer, [ 11 ] and renal cell carcinoma. Although BRCA is not typically considered an immunogenic disease, therapeutic strategies aimed at modulating the immune system are currently being tested in clinical settings. [ 12 ] Immune responses vary considerably among different subtypes of BRCA, and it is anticipated that not all BRCA patients will respond to immune checkpoint inhibitors targeting programmed cell death protein 1, programmed cell death ligand 1, or cytotoxic T lymphocyte (CTL) antigen 4. This limited efficacy may be attributed to alternative immune escape mechanisms employed by tumor cells. Therefore, there is an urgent and critical need to explore novel immunomodulatory molecules beyond the established subtypes for BRCA treatment. A growing body of research highlights the potential roles of tumor-infiltrating immune cells in both the prognostic evaluation of patients and the efficacy of antitumor immunotherapy, indicating that immune cell infiltration may play a significant role in tumor progression. [ 13 , 14 ] However, the molecular immune-related mechanisms underlying BRCA remain ambiguous.

Transforming acidic coiled-coil-containing 3 (TACC3), the 3rd member derived from the TACC family, is encoded by the TACC3 gene, which is located at the 4p16.3 region of chromosome 4. [ 15 ] Other family members consist of TACC1 and TACC2. As a centrosome/microtubule-associated protein, TACC3 contains a highly conserved coiled-coil domain at its C-terminus and plays a leading part in the regulation of centrosome integrity and mitotic spindle dynamics by targeting the Aurora A kinase during the mitotic phase of nuclear division. [ 16 – 18 ] The downregulation of TACC3 protein induces G1 arrest and cell death by activating p38-p53-p21 signaling and triggering a chromosome-mediated cellular stress response. [ 19 ] Moreover, accumulated evidence from subsequent studies has demonstrated that aberrant TACC3 expression frequently occurs in a wide spectrum of human malignancies, including gastric carcinoma, [ 20 ] glioblastoma, [ 21 ] liver hepatocellular carcinoma, [ 22 ] non-small cell lung cancer, [ 23 ] esophageal squamous cell carcinoma, [ 24 ] multiple myeloma, [ 25 ] and endometrial cancer, [ 26 ] and positively associates with advanced clinical stage, lymph node metastasis, tumor differentiation, and worse prognosis, which indicates that TACC3 might have a significant relationship with advanced features of cancers. TACC3 contributes to the activation of phosphatidylinositol 3-kinase/protein kinase B and extracellular signal-regulated kinase (ERK) signaling pathways, and consequently promotes epithelial-mesenchymal transition (EMT). [ 27 ] Additionally, the fibroblast growth factor receptor gene 3 and TACC3 (FGFR3–TACC3) have been identified to be a common chromosomal abnormality and associated with the pathogenesis and progression in a wide spectrum of solid tumor types by promoting cell proliferation. [ 28 – 32 ] These above-mentioned studies suggested that overexpression of TACC3 exerts a considerable influence on tumor progression and may serve as a novel biomarker and potential target for antitumor treatment. Nevertheless, it is worth noting that downregulation of TACC3 expression has also been widely observed in ovarian [ 33 ] and thyroid [ 34 ] cancers, indicating that there is no consensus on its role across various cancer types. Additionally, evidence indicates that cell proliferation, invasion, and the EMT pathway were promoted via the inhibition of TACC3 in BRCA, [ 35 ] yielding contradictory results. Therefore, the precise mechanisms and potential prognostic value associated with TACC3 remain unclear and warrant further investigation.

Our current study aimed to investigate the expression level of TACC3 in BRCA and to explore the potential biological mechanism associated with the poor prognosis of BRCA patients. This study presents the 1st multidimensional assessment of TACC3’s clinical utility, molecular characteristics, and immunotherapeutic potential in BRCA, providing transformative insights into its biological and therapeutic significance.

2. Materials and methods

2.1. Data acquisition and processing

The differential expression of TACC3 in BRCA was assessed by analyzing RNA sequencing and protein data from The Cancer Genome Atlas (TCGA) ( https://portal.gdc.cancer.gov/ ), Gene Expression Profiling Interactive Analysis [ 36 ] ( https://dctd.cancer.gov/programs/occpr ), and the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) [ 37 ] ( https://cptac-data-portal.georgetown.edu/cptacPublic ) databases. The TCGA database was utilized to obtain RNA-Seq data from BRCA patients, along with corresponding normal tissue gene expression data, relevant clinicopathological information (workflow type: HTSeq– fragments per kilobase of exon model per million mapped fragments), and immune system infiltrates. Subsequently, the HTSeq data format was converted from fragments per kilobase of exon model per million mapped fragments to transcripts per million reads for further analyses. All data analyses were conducted in accordance with the publication guidelines ( https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/using-tcga ). The protein–protein interaction network of TACC3 in BRCA was obtained from STRING [ 38 ] ( https://cn.string-db.org/ ).

2.2. Differentially expressed genes (DEGs) analysis

The expression data (HTSeq-Counts) were categorized into high and low expression groups based on the median TACC3 expression level. Subsequently, unpaired Student t tests were conducted using the DESeq2 R package (version 4.2.1). DEGs were filtered using the cutoff values of |log2-Fold Change (FC)| > 2.0 and an adjusted P value <.05, which were considered the thresholds for identifying DEGs.

2.3. Gene ontology (GO) enrichment analysis and gene set enrichment analysis (GSEA)

In this study, we aimed to elucidate the significant functional and pathway differences between low- and high-TACC3 expression subgroups through GO functional enrichment analysis and GSEA, utilizing the clusterProfiler package in R. DEGs between the different TACC3 expression groups were selected for analysis. GO analysis provides a standardized framework for describing gene products in terms of molecular function (MF), biological process (BP), and cellular component (CC). GSEA is a computational method used to evaluate whether a predefined set of genes exhibits statistically significant and consistent differences between 2 phenotypes. For each analysis, gene set permutations were performed 1000 times, with the TACC3 mRNA expression level serving as the phenotype label. The study utilized h.all.v7.0.symbols.gmt (Hallmarks) from the MSigDB Collections as the reference gene set. Gene sets with a false discovery rate <0.25 and an adjusted P value <.05 were considered significant for enrichment.

2.4. Immune infiltration analysis in tumor tissues

The single-sample gene set enrichment analysis (ssGSEA) method from the GSVA package in R was employed to explore the correlations between TACC3 expression and the signature genes of 24 distinct types of tumor-infiltrating immune cells. Additionally, a systematic analysis of the relative immune infiltration levels was conducted based on published literature. To assess the associations between different TACC3 expression groups and the infiltration levels of immune cells, Spearman correlation and Wilcoxon rank-sum tests were performed. Tumor Immune Estimation Resource (TIMER) is a comprehensive online resource platform for systematic analysis of tumor-associated immune infiltrates across diverse cancer types. [ 39 ] The abundances of 6 different kinds of tumor-infiltrating immune cells, including B cells, cluster of differentiation 4+ (CD4+) T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells, are estimated by the TIMER algorithm. TIMER allows users to input function-specific parameters, with resulting figures dynamically displayed to conveniently access tumor immunological, clinical, and genomic features. The web server can be visited at https://cistrome.shinyapps.io/timer/ . TIMER includes >10,000 samples from various cancer types in the TCGA database. In this study, we evaluated the relationship between TACC3 expression and immune infiltrates in BRCA samples.

2.5. Patients and tissue specimens

A cohort of BRCA tissue specimens was obtained from patients diagnosed at the Department of Pathology of the Second Affiliated Hospital, Shandong University from January to December 2018. All the specimens were identified as invasive carcinomas of unspecified type, with areas exhibiting preinvasive components, specifically ductal carcinoma in situ. Candidates were excluded due to incomplete or lost data, particularly in cases where the paraffin-embedded tissue blocks were not available for immunostaining. Finally, 181 eligible patients who underwent potentially radical surgical resection and complete axillary node clearance were identified for this study. For each case, malignancy grade, tumor size (maximum diameter), lymph node status, estrogen receptor (ER) status, progesterone receptor (PR) status, HER-2 status, and immunostaining at the time of diagnosis were evaluated. Tumors were graded according to a modified Elston and Ellis scoring system, [ 40 ] and tumor size was categorized in accordance with the TNM staging criteria. All pathological diagnoses of tissue specimens were performed independently by 2 qualified pathologists blinded to the clinical characteristics. This study was approved by the Ethics Committee of the Second Hospital of Shandong University, and informed consent was obtained from all enrolled patients prior to the collection of specimens in accordance with established guidelines.

2.6. Tissue microarrays (TMAs) and immunohistochemistry staining

TMAs were arranged in accordance with the previously established protocols. [ 41 – 44 ] In summary, H&E-stained sections from each tumor block were meticulously examined to identify the most characteristic tumor regions. Subsequently, 2 tissue cores (each with a diameter of 1 mm) were extracted from both the ductal carcinoma in situ and invasive regions of each case for the construction of TMAs. Following this, contiguous sections of 4 μm thickness were prepared from the array blocks for further analysis, including H&E staining and immunohistochemical evaluation. The tissue sections were deparaffinized using xylene, rehydrated with a graded alcohol series, and microwaved at 500 W for 2 × 5 min in 10 mM citrate buffer (pH 6.0). Following rinsing in tris-buffered saline (pH 7.6), the sections were treated with 3% hydrogen peroxide to block endogenous peroxidase activity, then incubated in 5% normal horse serum before overnight exposure at 4°C to a monoclonal primary antibody targeting TACC3. Subsequently, an appropriate secondary antibody and streptavidin-peroxidase conjugate were applied, and specific binding of the antibody was visualized using a solution of 3,3-diaminobenzidine. Finally, the sections were counterstained with hematoxylin and mounted with neutral balsam. Positive controls were included on each slide run. Normal fetus serum was used as a negative control by substituting for the primary antibody. The positive staining of TACC3 protein was observed in both the cytoplasm and nucleus.

All assessable TMA cores were read and scored semiquantitatively by 2 independent pathologists using a multiheaded light microscope in accordance with the Allred score method. [ 45 ] With this method, the intensity of the immunohistochemical reaction was recorded as 0 (negative, no staining at all); 1 (weak positive); 2 (moderately positive); or 3 (strongly positive), as shown in Figure 3 . The percentage was scored based on the area of positive staining cells to total tumor cells: 0 (none); 0%–25% (score 1); 26% −50% (score 2); 51% −75% (score 3); >75% (score 4). The final staining was scored based on the product of staining intensity and percentage of positive cells, which ranged from 0 to 12. Thus, BRCA specimens were classified into low-expression and high-expression groups according to the median value of TACC3 expression. The immunostaining slides of ER, PR, and HER-2 were reviewed and reevaluated. Tumors were counted as positive for ER and PR if >1% of the nuclei of neoplastic cells showed definitive staining. Kiel 67 status was scored low if ≥14% of the nuclei of neoplastic cells were positive. p53 was considered positive only when >75% of cancer cells had distinct, strong nuclear staining and negative when 75% or less were stained. [ 46 ] HER-2 was scored according to American Society of Clinical Oncology and College of American Pathologists Her-2 clinical practice guideline. [ 47 ]

2.7. Statistical analyses

The expression levels of TACC3 in both non-paired and paired samples were compared using the Wilcoxon rank-sum test and the Wilcoxon signed-rank test, respectively. The statistical significance of the relationship between TACC3 expression and clinicopathological variables in BRCA patients was analyzed using the Welch 1-way analysis of variance test and the chi-squared test. Receiver operation characteristics (ROC) curve analysis was performed to evaluate the diagnostic value of TACC3 expression using the pROC package. The calculated area under the curve (AUC) values ranged from 0.5 to 1.0, indicating discrimination capabilities between 50% and 100%. The optimal cutoff value was determined based on immunostaining scores that yielded the highest sum of sensitivity and specificity. Survival curves, including overall survival (OS), relapse-free survival, and disease-free survival across different TACC3 expression groups, were generated using the online Kaplan–Meier plotter tool [ 48 ] ( https://www.kmplot.com/analysis/ ). Additionally, univariate and multivariate Cox regression analyses were conducted to assess the prognostic value of TACC3 expression and to determine the associations between TACC3 and clinicopathological variables through Cox proportional hazard models. A 2-tailed P value ≤.05 was considered statistically significant in all tests. Statistical analyses were performed using R and SPSS software (version 26.0).

3. Results

3.1. The expression level of TACC3 was significantly unregulated in BRCA

Firstly, we examined the transcriptional levels of TACC3 in various types of human malignant tumors with those of normal samples using the RNA sequencing data obtained from the TCGA database. The results exhibited significant differences between tumor tissues and adjacent matched normal tissues in the vast majority of tumor types, as shown in Figure 1 A. The analysis results demonstrated that the expression level of TACC3 was significantly overexpressed in nearly all tumor types compared to the adjacent matched normal tissues, including urothelial bladder carcinoma, invasive breast carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, esophageal carcinoma, glioblastoma multiforme, head-neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, prostate adenocarcinoma, rectum adenocarcinoma, stomach adenocarcinoma, and uterine corpus endometrial carcinoma. Secondly, the protein expression levels of TACC3 in BRCA were also detected using several different databases, including TCGA, CPTAC, and gene expression profiling interactive analysis. The results indicated that the protein expression level of TACC3 was significantly upregulated in tumor tissues when compared to normal tissues (Fig. 1 B–D, all P < .001). Additionally, in pair-wise comparison analysis, the expression level of TACC3 in the BRCA tissues was also significantly higher than that detected in adjacent matched normal tissues (Fig. 1 E, P < .001). A box plot was used to analyze the expression levels of TACC3 mRNA in 1104 BRCA samples and 113 normal samples from the encyclopedia of RNA interactomes project, and the results showed that the TACC3 expression level was considerably upregulated in cancer tissues compared to normal breast tissues (Fig. 1 F, P < .001). Apart from that, the expression level of TACC3 also exhibited good distinguishing capability in the TCGA database, in which the AUC value of TACC3 was >0.80 (AUC: 0.960, 95% confidence interval: 0.942–0.977) (Fig. 1 G), suggesting that it might act as a promising biomarker to improve the diagnosis of BRCA.

3.2. Associations between TACC3 expression and clinicopathological variables in BRCA

To further evaluate the role of TACC3 expression in BRCA patients, we collected data from the TCGA database and analyzed the correlations between TACC3 expression and various clinicopathological variables using the chi-squared test. A total of 1222 patients were included in the study, and TACC3 mRNA expression levels were categorized into low- and high-expression groups based on the median value. The results indicated significant associations between TACC3 expression and several clinical parameters, including PR status (Fig. 2 A, P <.001), ER status (Fig. 2 B, P <.001), histological type (Fig. 2 E, P <.001), and pathologic T stage (Fig. 2 H, P <.001). However, no significant associations were found with Her-2 status (Fig. 2 C, P = .222), pathologic M stage (Fig. 2 D, P = .510), menopause status (Fig. 2 F, P = .115), PAM50 molecular subtyping (Fig. 2 G, P = .926), pathologic N stage (Fig. 2 I, P = .229), and pathological stage (Fig. 2 J, P = .104). Detailed information is presented in Table 1 . Subsequently, we conducted an immunohistochemistry staining analysis to assess the protein expression levels of TACC3 in BRCA specimens based on our own data source. Among the 181 cases, there were 37 cases with negative staining (Fig. 3 H), 63 cases with weak positive staining (Fig. 3 I), 45 cases with moderate positive staining (Fig. 3 J), and 36 cases with strong positive staining (Fig. 3 K). Based on the staining intensity, these cases were categorized into low (negative and weakly positive) and high (moderately positive and strongly positive) expression groups. The high TACC3 expression group comprised 81 cases, while the low TACC3 expression group included 100 cases. The protein expression levels of TACC3 were significantly correlated with ER ( P < .001), PR ( P = .001), HER-2 ( P = .003), Kiel 67 ( P = .027), tumor size ( P = .033), and tissue grade ( P < .001) (Table 2 ), which is consistent with the conclusions drawn from the public data mentioned earlier. However, it is important to note that there was no correlation between TACC3 protein levels and lymph node status ( P = .888, Table 2 ). Additionally, we further conducted a univariate Cox proportional hazards regression analysis, which revealed a significant association between high TACC3 protein expression and poorer OS (hazard ratio [HR] = 8.526, P < .001, Table 3 ), indicating that TACC3 may negatively affect the prognosis of BRCA patients. Similar results were also observed for ER, PR, and HER-2. Furthermore, multivariate regression analysis confirmed that TACC3 expression remained an independent prognostic factor for OS (HR = 6.966, P < .001, Table 3 ) in BRCA patients. These findings suggest that TACC3 may serve as an independent prognostic indicator for cancer risk and could potentially extend patients’ OS.

3.3. Potential Prognostic Value of TACC3 in BRCA

To assess the potential prognostic value of TACC3 expression levels in BRCA, we utilized data from the TCGA database to analyze the correlations between TACC3 expression and various patient survival rates using KM survival curve analysis. The results demonstrated that patients with low TACC3 expression experienced significantly better relapse-free survival (Fig. 3 A, P < .001), overall survival (Fig. 3 B, P = .0042), and distant metastasis-free survival (Fig. 3 C, P < .001) compared to those in the high TACC3 expression group. We also compared and analyzed the different expression levels of TACC3 between normal breast tissues and cancers (Fig. 3 D, P < .001), tumor size with a diameter <2cm or ≥ 2cm (Fig. 3 E, P < .001), and tissue differentiation degree (Fig. 3 F, P < .001). The ROC curve revealed that the expression of TACC3 had high accuracy (AUC 0.793, 95% confidence interval [CI] 0.734–0.852) for the diagnosis of BRCA (Fig. 3 G). As shown in Table 4 , the univariate Cox proportional hazards regression analysis revealed a significant association between high TACC3 expression and poorer overall survival (HR = 0.337, P < .001). Furthermore, multivariate analysis confirmed that TACC3 expression remained independently correlated with both overall survival and clinical stage within the TCGA datasets.

3.4. Functional enrichment analysis of low- and high-TACC3 expression samples in BRCA

The impact of TACC3 on the survival of BRCA patients implies its potential clinical application value. Subsequently, to explore the potential mechanisms of TACC3 that promote tumor progression, we examined the DEGs in the low TACC3 expression and high TACC3 expression samples obtained from the LinkedOmics database. Bounded by the median level of TACC3 expression, the subjects were classified into 2 groups: a low TACC3 expression group (0%–50%) and a high TACC3 expression group (50%–100%). The DEGs related to TACC3 were exhibited in a volcano plot (Fig. 4 A). After removing null values, the total number of DEGs was 42,265, with 4778 satisfying the thresholds of |log2(FC)| > 1 and P .adj < .05. Among these, 1285 DEGs were found in the high expression group (where logFC is positive), while 3493 were in the low expression group (where logFC is negative). Additionally, 2290 DEGs satisfied the criteria of |log2(FC)| > 1.5 and P .adj < .05, comprising 429 in the high expression group and 1861 in the low expression group. Furthermore, 1201 DEGs met the threshold of |log2(FC)| > 2 and P .adj < .05, with 158 in the high expression group and 1043 in the low expression group. The top 50 positively and negatively correlated genes were presented in heatmaps in Figure 4 B and C. Following this, we conducted gene ontology (GO) enrichment analysis to predict the functions of co-expressed DEGs in BRCA patients. To enhance the accuracy and stability of our results, we established a threshold of |log2(FC)| > 2 and P .adj < .05, which indicates a 4-fold difference in expression levels. The GO enrichment analysis encompasses 3 fundamental dimensions: BP, CC, and MF. In the biological processes category, the results indicated that DEGs were significantly enriched in pathways related to chromosome segregation, mitotic cell cycle phase transition, DNA recombination, and noncoding RNA processing, all of which showed positive correlations with TACC3. The remaining BP items that showed negative correlations with TACC3 included pattern specification process, negative regulation of locomotion, regulation of transporter activity, heat morphogenesis, response to transforming growth factor beta, and multicellular organismal signaling (Fig. 4 D). In terms of CC, there were positive correlations between TACC3 and the chromosomal region, mitochondrial inner membrane, and mitochondrial matrix. Conversely, several CC terms that are negatively correlated with TACC3 include cell leading edge, transporter complex, synaptic membrane, apical part of cell, endosome membrane, ciliary part, and extracellular matrix, all of which are also significant (Fig. 4 E). The top 5 enrichment items positively associated with TACC3 in the MF category included structural constituent of ribosome, catalytic activity acting on DNA, electron transfer activity, catalytic activity on RNA, and ATPase activity. Conversely, the genes negatively associated with TACC3 were those involved in growth factor binding, phospholipid binding, guanyl-nucleotide exchange factor activity, extracellular matrix structural constituents, and beta-catenin binding (Fig. 4 F).

We subsequently conducted a Kyoto Encyclopedia of Genes and Genomes pathway analysis to identify potential signaling pathways involved in the regulation of TACC3 in BRCA. The results suggested that these functions of DEGs positively related to TACC3 were mainly enriched in a variety of biological processes, including the spliceosome, ribosome, RNA transport, systemic lupus erythematosus, Parkinson disease, Epstein–Barr virus infection, and human T-cell leukemia virus 1 infection, while DEGs in neuroactive ligand-receptor interaction, Ras signaling pathway, and focal adhesion were negatively correlated with TACC3 (Fig. 4 G). Detailed data was listed in Table 4 . Additionally, we conducted a functional protein–protein interaction network analysis of TACC3 by using STRING. The top 10 functional partner proteins were screened out with a high degree of connectivity, including KIF22, AURKA, NOTCH3, BUB1B, PLK1, CLTC, CDCA8, NOTCH4, FGFR3, and CKAP5 (Fig. 4 H). According to the results from GO and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, the associated proteins play key roles in a series of biological processes, including organelle fission, nuclear division, mitotic sister chromatid segregation, mitotic nuclear division, chromosome segregation, and microtubule cytoskeleton organization during mitosis. Other implicated processes include the cell cycle, cell cycle checkpoints, the humoral immune response, and the regulation of transcription during the G1/S transition of the mitotic cell cycle (Fig. 4 I). Additionally, we conducted GSEA analysis to pinpoint key pathways associated with TACC3. The GSEA results revealed that the most significantly enriched pathways, meeting the criteria of false discovery rate < 0.25 and P < .05, included the cell cycle (Fig. 5 A and E), resolution of sister chromatid cohesion (Fig. 5 B), mitotic prometaphase (Fig. 5 C), and separation of sister chromatids (Fig. 5 D).Consistently, the density plot (Fig. 5 F) confirmed that all these mitosis- and cell cycle-related pathways exhibited robust enrichment at comparable levels.

3.5. The correlation between TACC3 expression and the infiltration of immune cells

Given that both GO and GSEA enrichment analysis indicated that TACC3 plays a role in tumor-associated immune regulation, we further assessed whether the expression level of TACC3 was associated with immune cell infiltration. We used ssGSEA from the R package with Spearman test to investigate the potential correlation between the TACC3 mRNA expression level and 24 types of immune cell infiltration levels in BRCA. The results revealed that TACC3 mRNA expression was positively correlated with the immune infiltration of effector memory T cells, T follicular helper cells, T cells, dendritic cells (DC), cytotoxic cells, B cells, T helper 1 (Th1) cells, natural killer (NK) CD56dim cells, activated DC (aDC), regulatory T cells (Treg), and Th2 cells (all P <.05), but was negatively correlated with the infiltration of mast cells, eosinophils, Th17 cells, NK cells, central memory T cells (Tcm), immature DC (iDC), plasmacytoid dendritic cells (pDC), and NK CD56bright cells (Fig. 6 A). We further examined the relationship between the different TACC3 expression level groups and the aforementioned immune cell infiltration. The results demonstrated a significant difference in infiltrating immune cell levels when TACC3 expression was classified into low and high groups (Fig. 6 B). The results revealed that 17 major types of immune cells, including mast cells, eosinophils, Th17 cells, NK cells, Tcm, iDC, pDC, T cells, B cells, Th1 cells, NK CD56dim cells, aDC, Treg, and Th2 cells, showed significant infiltration differences between the high TACC3 group and the low TACC3 group in BRCA tissues (all P <.05). In addition, analyses using TIMER showed that TACC3 mRNA expression was positively correlated with immune infiltration of Th2 cells (Fig. 6 C, r = 0.539, P <.001), Treg (Fig. 6 D, r = 0.311, P <.001), aDC (Fig. 6 E, r = 0.294, P <.001), and NK CD56dim cells (Fig. 6 F, r = 0.256, P <.001). Conversely, TACC3 expression was negatively correlated with that of mast cells (Fig. 6 G, r = 0.309, P <.001) and eosinophils (Fig. 6 H, r = 0.250, P <.001). Collectively, these findings from our study suggested that TACC3 may have a significant impact on the infiltration of immune cells in BRCA.

4. Discussion

To date, it is widely recognized that BRCA remains a leading cause of cancer-related deaths among women worldwide. Despite advancements in prevention, diagnosis, and prompt, effective treatment, the morbidity and mortality rates associated with BRCA continue to rise. Moreover, advanced BRCA patients who have undergone systemic therapy experience high rates of recurrence and metastasis, resulting in poor prognoses. This continues to pose a significant challenge for clinicians. [ 49 ] For instance, tamoxifen, a well-established ER blocking agent, reduces the recurrence and mortality rates by 50% in ER-positive patients. However, approximately one-third of these patients will develop intrinsic or acquired drug resistance to the drug. [ 50 ] Considering strong heterogeneity and genetic alterations that accumulate during tumorigenesis, identifying novel independent prognostic factors is crucial for a substantial proportion of patients with advanced BRCA. Such discoveries may offer valuable insights and effective guidance for clinical treatment.

In this study, a pan-cancer atlas exhibited that the expression level of TACC3 was markedly upregulated in a cohort of malignancies, comparing tumor and normal tissues from multiple independent datasets in different public databases. To investigate and better understand the roles of TACC3 expression in BRCA, we assessed the correlations between TACC3 expression and the clinicopathological characteristics of patients with BRCA. The findings showed that TACC3 expression was significantly associated with the T stage, histological type, ER, and PR status, respectively. Furthermore, we enhanced the reliability of our research by validating these findings through immunohistochemistry analysis using clinical samples. Additionally, the ROC curve revealed that the expression level of TACC3 also exhibited an outstanding capability of distinguishing BRCA tumor tissues from normal tissues based on data from TCGA, suggesting that it might help to screen for high-risk patients with BRCA and determine more aggressive treatment regimens. This study also showed that an increased expression level of TACC3 was an independent and unfavorable prognostic factor affecting the survival time of patients with BRCA, indicating its great potential as a novel prognostic biomarker and promising target for therapeutic interventions. These results are consistent with previous studies. Sun et al demonstrated that TACC3 was overexpressed along with glioma grade and could serve as an independent negative prognostic biomarker for glioma patients by comprehensively analyzing publicly available data. [ 51 ] Du et al discovered that TACC3 knockdown attenuated cell proliferation, colony formation, migration, and invasion capability, and promoted tumorigenesis and progression in colorectal cancer, eventually resulting in poor prognosis. [ 52 ] Yun et al further identified that elevated TACC3 expression significantly correlated with aggressive characteristics including tumor stage, lymph node status, poorly differentiated subgroup, tumor relapse, and shortened overall survival, and could be used as an independent prognostic factor in patients with gastric cancer. [ 20 ] Li and his colleagues also found that TACC3 may predict poor clinical outcomes in patients with prostate cancer, and its increased expression was significantly linked to a worse survival rate. [ 53 ] The aberrant expression of TACC3 has proven to be effective in differentiating tumors from normal tissues and in predicting patient survival rates, underscoring its potential as a clinical diagnostic and prognostic biomarker for BRCA. Consequently, our study stamped a new interpretation on the potential value of TACC3 in BRCA. However, the antitumor roles of TACC3 and its underlying molecular mechanisms have yet to be fully elucidated. Recent evidence indicated that stable cell lines harboring FGFR3-TACC3 gene fusions exhibit the expression of active FGFR fusion kinases, along with the activation of downstream mitogen-activated protein kinase ERK1/2 and transcription factor STAT1. [ 54 ] Besides, Ha et al have reported that overexpression of TACC3 can increase the expression level of Snail and Slug, as well as the transcriptional activity of β-catenin, thereby promoting an EMT phenotype through activation of phosphatidylinositol 3-kinase/protein kinase B and ERK signaling pathways. [ 27 ] As a critical component of comprehensive antitumor therapies, immunotherapy has garnered a great deal of attention and has a profound impact on the prognosis of malignant tumors. Given the considerable variability in the efficacy of immunotherapeutic agents across patients and different tumor types, identifying reliable biomarkers to effectively monitor responses to immunotherapy is essential for advancing precision in immune-oncology. In this study, we discovered that TACC3 plays a role in the biological processes that govern immune cells entering tumor tissues, which subsequently influences the development of BRCA and patient prognosis. This finding suggests that TACC3 has great potential as an immune-related biomarker in BRCA.

Increasing amounts of studies have revealed that TME plays important roles in tumor growth, progression, and the effect of immunotherapy. [ 55 ] TME consists of nonmalignant cells of the tumor such as cancer-associated fibroblasts, mesenchymal stromal cells, endothelial cells and pericytes composing tumor vasculature, immune and inflammatory cells, bone marrow-derived cells, and the extracellular matrix establishing a complex cross-talk with the tumor. [ 56 ] Different types of tumors and their subtypes have specific TMEs characterized by distinct immune cell infiltration and interaction with tumor cells. Immune infiltration consists of cell populations derived from lymphoid and myeloid lineages, thus constituting a pool of cells enabling innate and adaptive immunity mechanisms. Studies regarding BRCA-related TME have led to identifying various immune cell subpopulations, including tumor-associated neutrophils, macrophages, dendritic cells, mast cells, NK cells, myeloid-derived suppressor cells, and tumor-infiltrating lymphocytes, having antitumor or pro-tumor characteristics which have been correlated with BRCA aggressiveness and prognosis. The presence of tumor-infiltrating lymphocytes in the TME forms an obstacle to T-cell-based therapy for certain subtypes of BRCA. The previous study has revealed that TACC3 inhibition revives Trastuzumab emtansine (T-DM1)-induced immunogenic cell death by inhibiting microtubule polymerization and causing mitotic cell death that ultimately increases cytotoxic T-cell infiltration, thereby overcoming T-DM1 resistance in HER-2-positive BRCA. [ 57 ] This finding suggests that TACC3 may play a role in the immune response. Therefore, further investigation is warranted to explore how TACC3 interacts with the tumor microenvironment and influences tumor-infiltrating immune cells in BRCA. The ssGSEA methods demonstrated positive correlations between TACC3 expression and the immune infiltration of effector memory T cells, T follicular helper cells, T cells, DC, cytotoxic cells, B cells, Th1 cells, NK CD56dim cells, aDC, Treg, and Th2 cells, but was negatively correlated with the infiltration of mast cells, eosinophils, Th17 cells, NK, Tcm, iDC, pDC, and NK CD56bright cells. The results obtained from TIMER analysis indicated that TACC3 exerted a powerful influence on tumor-infiltrating immune cells in BRCA. The expression level of TACC3 exhibited negative correlations obviously with mast cells and eosinophils, conversely, was positively correlated with the infiltration of various immune cells, including Th2 cells, Treg, aDC, and NK CD56dim cells.

As the cytotoxic lymphocytes, NK cells are identified by the expression of the CD56 marker and classified into subpopulations based on the intensity of this marker: CD56dim (NK1 cells) and CD56bright (NK2 cells). The production of various inflammatory cytokines by both subsets of NK cells plays a crucial role in the antitumor immune response, as these cells interact with other cell types, including DCs, tumor-associatedmacrophages, T cells, and endothelial cells. This interaction predominantly occurs through the secretion of tumor necrosis factor-α and interferon-γ, which subsequently induces the activation of T-helper 1 (Th1) cells and CTLs against cancer cells. [ 58 ] Additionally, numerous studies have shown an inverse correlation between high levels of circulating or tumor-infiltrating NK cells and the presence of metastases at clinical presentation in patients with various cancer types. [ 59 , 60 ] Along similar lines, elevated expression levels of NK cell activating receptors and enhanced NK cell cytotoxicity have been associated with a favorable prognosis in multiple cohorts of cancer patients with or at risk of metastatic disease. Furthermore, NK cells seem to play a significant role in shaping developing tumors even in the absence of adaptive immunity. [ 61 , 62 ] DCs are the most potentantigen-presenting cells, and their infiltration enhances the presentation of tumor-associated or tumor-specific antigens to T cells, thereby initiating primary immune responses. [ 63 ] Tumor immune evasion remains a significant obstacle in contemporary immunotherapy. Various factors contribute to immune evasion, even within an immunocompetent environment, and these factors are involved at different stages of the immune cycle. Tregs function as a double-edged sword, both maintaining immune homeostasis and inhibiting immune responses in various disease contexts. Tumor cells evade immune surveillance by interacting with programmed cell death protein 1, programmed cell death ligand 1 or CTLA-4, which not only promotes the differentiation and activation of Tregs but also suppresses the growth and function of T effector cells. [ 64 ] Furthermore, Tregs contribute to acquired immune resistance by facilitating compensatory mechanisms within the TME that allow tumors to evade antitumor effects. [ 65 ] Previous studies have shown that the infiltration of Th2 cells is linked to tumor immune evasion and poor prognosis across various human malignancies. [ 66 , 67 ] In our study, we found Th2 and Treg cells are significantly increased, which suggests that TACC3 may help to mediate immune evasion in BRCA. Further research on TACC3 is anticipated to deepen our understanding of the factors involved in immune evasion and to enhance the efficacy of antitumor immunotherapy. Meanwhile, CD8+ cytotoxic T cells have been recognized as the primary effectors in the destruction of tumor cells, and their elevated levels of infiltration were positively correlated with improved clinical outcomes. [ 68 ] However, during tumor progression, CTLs often undergo depletion and dysfunction due to immunosuppression and immune-related tolerance within the TME, which contributes to adaptive immune resistance. Conversely, the priming of CD8+ T cells is fundamentally linked to interactions with innate immune cells, such as NK cells and DCs, as well as CD4+ T cells within the adaptive immune response. Upon activation, effector CTLs typically infiltrate the tumor site or its core, playing a crucial role in the elimination of cancer cells. Consequently, developing therapeutic strategies aimed at enhancing the infiltration of immune cells into the TME may provide a promising approach for immunotherapy in BRCA.

While the findings of this study provide novel insights into the association between TACC3 expression and BRCA, several limitations warrant further investigation. Firstly, most of our results were derived from bioinformatics analyses based on data from public databases, which limits their validation due to the lack of in vitro and in vivo experimental data. Clinical research involving larger patient cohorts and long-term follow-up is essential to clarify the correlations between TACC3 expression and clinical characteristics, potentially positioning TACC3 as a promising prognostic factor. Additionally, the critical signaling pathways associated with TACC3 require further validation. Although the significant roles of TACC3 in regulating immune cell infiltration during BRCA tumorigenesis have been discussed, future studies should focus on elucidating the detailed mechanisms through which TACC3 influences immune cell infiltration.

5. Conclusion

In conclusion, our study demonstrated a significant increase in TACC3 expression levels in BRCA, and the elevated TACC3 expression was associated with a decreased survival rate. Enrichment analysis indicated that TACC3 may function as an oncogenic factor, facilitating the infiltration of immune cells into the TME, thereby positioning it as a potential biomarker for immunotherapy. Consequently, these findings presented in this study provide compelling evidence that TACC3 holds substantial promise as both a novel prognostic factor and a valuable therapeutic target for BRCA.

Author contributions

Data curation: Xingchen Zhou.

Resources: Chengjun Zhou.

Validation: Xiangxiang Wu.

Writing – original draft: Zhishuang Li.

Writing – review & editing: Hui Li.

Abbreviations: AUC area under the curve BP biological process BRCA breast cancer CC cellular component CD cluster of differentiation CTL cytotoxic T lymphocyte, CPTAC Clinical Proteomic Tumor Analysis Consortium databases DCs dendritic cells DEG differentially expressed genes EMT epithelial-mesenchymal transition ER estrogen receptor ERK extracellular signal-regulated kinase FGFR3 fibroblast growth factor receptor gene 3 GO gene ontology GSEA gene set enrichment analysis HER-2 human epidermal growth factor receptor-2 HR hazard ratio KEGG Kyoto Encyclopedia of Genes and Genome MF molecular function NK natural killer OS overall survival PR progesterone receptor ROC receiver operating characteristic analysis ssGSEA single-sample gene set enrichment analysis TACC3 transforming acidic coiled-coil-containing 3 TCGA The Cancer Genome Atlas Tcm central memory T cells Th T helper TIMER Tumor Immune Estimation Resource TMA tissue microarrays TME tumor microenvironment Treg T regulation

The authors have no funding and conflicts of interests to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Li Z, Zhou X, Wu X, Li H, Zhou C. TACC3, correlated with immune cell infiltrates, is a novel prognostic biomarker in BRCA. Medicine 2026;105:30(e49943).

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