Aurka orchestrates colorectal cancer progression through dual regulation of tumor proliferation and immune microenvironment

Aurka orchestrates colorectal cancer progression through dual regulation of tumor proliferation and immune microenvironment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Aurka orchestrates colorectal cancer progression through dual regulation of tumor proliferation and immune microenvironment Yidong Xu, Wei Wang, Jiazi Yu, Jianpei Zhao, Xiaoyu Dai, Zhongchen Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7001456/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 Background : The third most common malignancy globally, colorectal cancer (CRC), remains the leading cause of cancer-related mortality despite significant therapeutic advancements. Aurora Kinase A (AURKA) functions as a key molecular hub, influencing both its canonical role in cell proliferation and its modulation of the tumor microenvironment (TME). Although its dual roles in colorectal tumorigenesis remain partially characterized. Methods : We performed an integrated analysis of AURKA expression using bulk and single-cell RNA sequencing datasets in CRC. Immune cell infiltration was quantified via CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). Prognostic significance was evaluated using Kaplan–Meier survival analyses. Clinical validation employed hematoxylin and eosin (HE) staining and immunohistochemistry (IHC) on CRC specimens. The association between AURKA and immunotherapy response was further investigated using publicly available immune checkpoint blockade (ICB) cohorts and a murine CRC model. Results : Pan-cancer analysis revealed AURKA regulation across gastrointestinal malignancies, with CRC exhibiting unique prognostic associations (P < 0.05). Elevated AURKA expression correlated with improved survival outcomes (median OS: 68 vs. 42 months; log-rank P =0.034). Pathway enrichment implicated AURKA in core cell cycle regulation (G2/M checkpoint, E2F targets) and immune-modulatory pathways (leukocyte migration, IL-2/STAT5 signaling). Validation experiment confirmed AURKA upregulation in CRC tissues and its positive correlation with CD4⁺ T-cell infiltration (transcriptomics: r = 0.62, P < 0.001; IHC: r = 0.60, P < 0.05). scRNA-seq resolution identified AURKA dominance in T proliferating cells. High AURKA predicted increased CD4⁺ activated memory T cells and prolonged survival in anti-PD-1 responders (HR = 0.44, P = 0.003). And murine model validation demonstrated elevated AURKA in immunotherapy responders, paralleling CD4⁺ memory T-cell expansion. Conclusion : AURKA serves as a dual modulator of tumor proliferation and immune engagement in CRC. Its expression reflects its role within the tumor microenvironment (TME) and T cell activity, with implications for targeting anti-PD-1 responses. colorectal cancer AURKA tumor immune microenvironment single-cell RNA sequencing immune cell infiltration immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Colorectal cancer (CRC) ranks as the third most prevalent malignancy worldwide, constituting 7% of new cancer diagnoses and 11% of cancer-related mortality according to GLOBOCAN 2020 estimates 1 – 3 . While surgical resection combined with chemotherapy remains the cornerstone of clinical management, the 5-year survival rate plummets from 65.1% in localized disease to 26% in metastatic presentations 4 , 5 , highlighting the urgent need for innovative therapeutic strategies and reliable prognostic biomarkers. Emerging evidence underscores the pivotal role of tumor microenvironment (TME) immunodynamics in carcinogenesis, in which the balance between tumor-antagonizing and tumor-promoting immune populations dictates disease progression. The antitumor armamentarium comprises CD8 + cytotoxic T lymphocytes (CTLs), effector CD4 + T cells, natural killer cells, and activated dendritic cells (aDCs) - the latter orchestrating CTL recruitment through CXCL9/10-CXCR3 chemokine axis activation 6 – 10 . CD4 + T cells potentiate antitumor immunity through two mechanisms: direct CTL activation via CD40-CD40L interactions and indirect DC licensing, which enhances tumor antigen cross-presentation 11 – 13 . Conversely, immunosuppressive elements, including regulatory T cells (Tregs) and myeloid-derived suppressor cells, dominate pro-tumorigenic niches 14 . Foxp3 + Tregs maintain immune homeostasis through IL-10/TGF-β-mediated suppression yet paradoxically facilitate immune evasion by dampening CTL cytotoxicity 15 , 16 . Aurora Kinase A (AURKA) is a critical molecular nexus in the immunological landscape. This serine/threonine kinase, localized to centrosomes and spindle microtubules, regulates mitotic fidelity through centrosome maturation and chromosomal segregation 17 – 20 . Beyond its canonical proliferative role, AURKA exhibits TME-modulating properties. Preclinical studies have demonstrated that AURKA inhibition paradoxically upregulates PD-L1 expression while impairing immune cell infiltration, a mechanistic conundrum potentially explaining immunotherapy resistance 21 . Furthermore, AURKA-driven epithelial-mesenchymal transition promotes metastatic dissemination in CRC models 22 , positioning it as a multi-faceted therapeutic target. In this study, we employed an integrated multi-omics approach combining bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) with single-cell RNA-seq datasets from the TISCH2 atlas. This approach enabled systematic profiling of AURKA expression across CRC molecular subtypes and its correlation with clinicopathological determinants, including TP53 mutation status and microsatellite instability (MSI). Survival analyses revealed the prognostic value of AURKA across multiple endpoints, including overall survival (OS), disease-free survival (DFS), recurrence-free survival (RFS), and progression-free survival (PFS). The immunological functions of AURKA in immune cell infiltration were assessed using CIBERSORT, single-sample gene set enrichment analysis (ssGSEA), and immunohistochemistry (IHC). The relationship between AURKA expression and response to immune checkpoint blockade were analyzed in both human cohorts and a murine CRC immunotherapy model. These analyses revealed a positive association between AURKA expression and CD4⁺ T cell infiltration, and enhanced CD4⁺ activated memory T cell abundancein immunotherapy responders, positioning AURKA as a dual biomarker-prognostic for survival outcomes and predictive for ICB response in CRC. Methods Pan-Cancer Analysis Using TCGAplot Pan-cancer transcriptional profiling was conducted using the TCGAplot R package (v8.0.0), which integrates paired/unpaired TPM-normalized expression matrices from TCGA. The analysis pipeline comprised three complementary approaches: 1) Tumor-normal comparisons across 33 cancer types were visualized through unpaired analyses using the pan_boxplot function; 2) Paired analyses restricted to 15 malignancies with ≥ 20 tumor-normal sample pairs were executed via pan_paired_boxplot; 3) Tumor-specific expression patterns across all 33 cancers were mapped using pan_tumor_boxplot. The diagnostic potential of candidate genes was quantified using ROC curve analysis (roc_curve function), with AUC values reflecting tumor-normal discrimination accuracy. Single-Cell Analysis via TISCH2 Single-cell resolution analysis of AURKA expression heterogeneity was performed using the Tumor Immune Single-Cell Hub 2 (TISCH2; http://tisch.comp-genomics.org ) by integrating four CRC-specific scRNA-seq datasets. These included primary tumors, liver metastases, matched normal tissues, and peripheral blood profiles on the Smart-seq2 and 10X chromium platforms. Data pre-processing prioritized cell populations with definitive markers: malignant epithelia (EPCAM+/KRT18+), immune subsets (CD45 + T/B/myeloid cells), and stromal fibroblasts (COL1A1+/ACTA2+). Harmony batch correction enabled cross-dataset comparisons of AURKA expression (normalized transcripts/10,000 UMIs), stratified by: (1) Cellular compartment: Tumor vs. normal epithelia (GSE108989, GSE139555) and stromal-immune interactions (GSE146771); (2) Metastatic context: KRAS^(G12D)-mutant liver metastases under T-cell therapy (GSE136394); (3) Therapeutic perturbation: Myeloid-targeted responses to anti-CSF1R/CD40 agonists (GSE146771). The differential expression was assessed using the MAST framework. Mutation-Associated Expression Analysis via Tumor Immune Estimation Resource 2.0 (TIMER2) The TIMER2 (TIMER2; http://timer.cistrome.org ) was used to assess the association between TP53 mutations and AURKA expression in CRC. Specifically, the "Gene_Mutation" module was employed to compare transcriptional profiles of TP53-mutated and wild-type subgroups, with parallel analyses performed for APC, KRAS, and TTN mutations. Transcriptomic differences in AURKA expression were quantified using a two-sample Wilcoxon test, with statistical significance defined as p < 0.05. The somatic mutation status of all genes was derived from TCGA-COAD datasets using standardized mutation annotation format (MAF) files. Histological Subtype Analysis via UALCAN UALCAN ( http://ualcan.path.uab.edu ) was used to assess AURKA expression across the CRC histological subtypes (adenocarcinoma vs. mucinous adenocarcinoma) and tumor-adjacent normal tissues. To conduct this analysis, AURKA was meticulously searched in the TCGA gene panel search box. Subsequently, the "Colon adenocarcinoma" dataset was precisely selected from the TCGA dataset to access the dedicated expression analysis page. Clinical Correlations and Pathway Enrichment via Biomarker Exploration of Solid Tumors (BEST) Platform The BEST (BEST; https://rookieutopia.hiplot.com.cn ) platform was used to delineate AURKA's clinical relevance and functional implications of AURKA in CRC pathogenesis. By leveraging harmonized multi-omics data from TCGA, GEO, ICGC, CGGA, and ArrayExpress repositories, we conducted stratified analyses across tumor progression, TNM staging (I-IV), clinical stage (early vs. advanced), histopathological features (WHO histological subtypes), and microsatellite instability (MSI) status (MSS/MSI-H). Significant differences in AURKA expression between clinical subgroups were determined using platform-implemented ANOVA with Tukey's post-hoc test ( p < 0.05). Functional enrichment analyses (Gene Ontology [GO], Kyoto Encyclopedia of Genes and Genomes [KEGG], and Gene Set Enrichment Analysis [GSEA]) were conducted to identify biological processes and pathways linked to AURKA dysregulation. Multi-Omics Co-expression Analysis via LinkedOmics Multi-omics co-expression networks were constructed using LinkedOmics ( http://www.linkedomics.org ) by analyzing 391 TCGA colon adenocarcinoma RNA-seq profiles. The workflow included: 1) cohort selection (CRC), 2) data configuration (gene-level quantification), 3) target specification (AURKA), and 4) Spearman's correlation analysis (|r| >0.3, p < 0.05) to identify significant interactors. Survival Analysis Using Kaplan – Meier Plotter The clinical prognostic value of AURKA transcripts (probes: 208079_s_at, 208080_s_at, and 204092_s_at) was assessed using Kaplan–Meier Plotter ( http://kmplot.com ) with RNA-seq data from patients with CRC. Optimal expression cutoffs were determined using maximal survival discrimination (minimum P-value method). Log-rank tests were used to compare OS and PFS between the high and low expression groups (significance threshold: P < 0.05). Immunohistochemistry (IHC) and Hematoxylin and Eosin (HE) Staining Formalin-fixed human colorectal cancer tissue specimens and matched adjacent peritumoral samples were collected from Ningbo No.2 Hospital between January 2022 and January 2024. Every participant signed informed consents before this study implementation. This study was approved by the Ethical Review Board of Ningbo No.2 Hospital (YJ-NBEY-KYSB-202308101). The inclusion criteria are that at least two pathologists confirmed the histopathological diagnosis is colon cancer according to the World Health Organization’s histopathological classification criteria. CRC tissues and adjacent normal tissues were fixed in 4% paraformaldehyde at 4°C overnight, and then embedded in paraffin and sectioned (7-µm thickness). HE staining of the sections was performed. Immunohistochemical staining was performed using the Envision IHC kit (Maxin Biotechnologies Inc., Fuzhou, Fujian, China). Tissue sections were subjected to IHCfor AURKA(1:1000; Proteintech, China), Ki67(MXR002; Maixin Biotechnology Inc., Fuzhou, China), and CD4(MXR036; Maixin Biotechnology Inc., Fuzhou, China). Ki67 IHC was used to evaluate the proliferation of tumor cells and proliferating T cells. IHC staining was evaluated semi-quantitatively by assessing both the proportion of positively stained tumor cells and the staining intensity under light microscopy. Staining intensity was classified into four grades: 0 (no staining), 1 (weak, light yellow), 2 (moderate, yellow-brown), and 3 (strong, dark brown). The percentage of positive tumor cells was scored as follows: 0 ( 75%). A composite IHC score was calculated by multiplying the intensity score by the corresponding percentage score for each intensity category. The final score for each specimen was obtained by summing these products across all intensity levels: IHC score = Σ (intensity × percentage score at that intensity). The immunohistochemical procedure was as follows. First, the slides were deparaffinized. After the antigen was recovered by high-pressure heating with citrate buffer (Maxin Bio), tissues were incubated with different antibodies at 4°C overnight and HRP-polymer secondary antibodies (Maxin Bio) for 15 min, and then incubated and developed using DAB solution (Maxin Bio). Immunotherapy Response Analysis via TISMO database The TISMO database ( http://tismo.cistrome.org ) is a publicly accessible resource that provides transcriptomic profiles of syngeneic mouse tumor models. It offers standardized analyses of gene expression, immune cell infiltration, and pathway enrichment under various immunotherapeutic conditions, including anti-PD-1, anti-PD-L1, anti-CTLA-4, and interferon treatments. All data were uniformly processed using the RIMA pipeline, which ensures high-quality control, batch effect correction, and immune cell deconvolution, enabling consistent and reliable comparisons across studies. Leveraging this resource, we systematically examined the association between AURKA expression and response to immune checkpoint blockade in murine CRC models. Statistical analysis Statistical details were available in the figure legends. Unpaired/paired Student’s t test (2-tailed) were used to analyze differences between two groups. The Kaplan-Meier method was used to perform the survival curves, and the Log-rank test was used to conduct the statistical analysis. Fisher’s exact test was used for the IHC quantification of AURKA expression in COAD tissues and normal tissues. The data were shown as mean ± standard deviation (SD) unless otherwise noted. All statistical analysis were performed in GraphPad Prism 9. P value < 0.05 was considered significantly. Results mRNA Expression and Different Clinical Parameters of AURKA in CRC We first examined the differences in AURKA mRNA expression levels between human tumor tissues and their corresponding normal counterparts using the TCGAplot. The results showed that AURKA was significantly upregulated in most human cancer types, including breast cancer, colon cancer (COAD), glioblastoma, and liver cancer (LIHC), and downregulated in thyroid cancer (Fig. 1 a–b). Although pan-cancer analysis revealed consistent AURKA overexpression across multiple malignancies, gastrointestinal cancers exhibited the most pronounced upregulation (fold change > 2, p < 0.05) (Fig. 1 c). IHC staining confirmed that AURKA expression was significantly elevated in CRC tumor tissues compared to that in adjacent non-tumorous tissues (Fig. 1 d). As CRC exhibits profound genomic heterogeneity and an elevated mutation burden 23 , this dysregulation may be mechanistically linked to somatic mutations within its regulatory architecture, highlighting the necessity for comprehensive mutational profiling in CRC pathogenesis research. Mutational landscape analysis of CRC samples from TCGA revealed that the most frequently mutated genes in CRC were APC (74%), TP53 (53%), KRAS (43%), and TTN (36%) (Figure S1 a). Notably, patients with CRC harboring TP53, APC, or TTN mutations exhibit significantly altered AURKA expression compared to those without these mutations. In contrast, AURKA expression did not significantly differ between patients with and without KRAS mutations (Fig. 1 e). These results suggested that frequently mutated genes contribute to the regulation of AURKA expression in CRC. Furthermore, we investigated the correlation between AURKA mRNA expression and various clinicopathological parameters, including cancer stage, sex, age, histological subtype, MSI, and nodal metastasis status. Our analysis revealed that AURKA expression was significantly higher in adenocarcinomas than in mucinous adenocarcinomas (Fig. 1 f). We also found that high AURKA expression was linked to microsatellite stability (MSS), indicating that AURKA may be involved in the progression of CRC with different microsatellite statuses (Fig. 1 g). The results showed no association between AURKA mRNA expression and CRC stage, sex, age, or nodal metastatic status (Fig. 1 h; Figure S1 b–f). AURKA expression is associated with genomic alterations and clinical parameters, suggesting a role in the pathogenesis and progression of CRC. Diagnostic and Prognosis Value of AURKA mRNA in CRC Previous studies reported conflicting results regarding the relationship between AURKA expression and prognosis in CRC 24 , 25 . Receiver operating characteristic (ROC) analysis established a strong diagnostic capacity of AURKA in CRC, yielding an area under the curve (AUC) of 0.953 (95% CI: 0.931–0.972) for distinguishing patients with high AURKA expression from controls with low AURKA expression (Fig. 2 a). Survival analyses demonstrated significant clinical correlations: patients with elevated AURKA expression exhibited prolonged OS (median OS: 68 vs. 42 months; log-rank P = 0.034, Fig. 2 b), along with improved DFS (HR = 0.62, 95% CI 0.51–0.75), RFS (HR = 0.66, 95% CI 0.47–0.72), and PFS (HR = 0.56, 95% CI 0.42–0.76) compared to the low-expression group (Fig. 2 c–e). These multimodal findings suggested that AURKA is a dual-function biomarker for CRC diagnosis and outcome prediction. Co-expressed Genes of AURKA and Functional Enrichment Analysis in CRC from the LinkedOmics Database The LinkedOmics database (website: http://www.linkedomics.org/login.php ) was used to identify significant correlations with AURKA. This database integrates multi-omics data, including next-generation sequencing results from TCGA, focusing on gene expression and functional roles within the TME. Spearman's correlation analysis showed that the genes correlated with AURKA (Fig. 3 a). The heatmap shows 50 genes that were positively or negatively associated with AURKA (Fig. 3 b–c). GO analyses showed that AURKA was associated with various biological processes and pathways including mitotic sister chromatid segregation, ribosome biogenesis, regulation of leukocyte migration, and leukocyte tethering or rolling (Fig. 3 d). AURKA was also associated with the cell cycle, DNA replication, proteasome, and ubiquitin-mediated proteolysis from the KEGG molecular pathways (Fig. 3 e). We also performed a GSEA pathway enrichment analysis to explore the biological functions of AURKA. The results indicated that AURKA is primarily involved in several biological processes and pathways, including the G2/M checkpoint, DNA repair, mitotic spindle, inflammatory response, Myc target v1, and unfolded protein response (Fig. 3 f–k). AURKA mRNA Expression in CRC from TISCH2 Given the complexity of the TME, we utilized single-cell RNA sequencing datasets, including GSE108989, GSE136394, GSE139555, GSE146771, and GSE166555 (Table 1 ) to comprehensively analyze its cellular composition and functional dynamics and found that AURKA was mainly expressed in T proliferating cells, many other immune cells, and tumor cells (Fig. 4 a–d). T proliferating cells represent a subset of T lymphocytes that exhibit robust proliferative activity, typically in response to antigen stimulation, cytokine signaling, or immune activation. These cells are critical for antitumor immune responses. In the TME, T proliferating cells exhibit increased infiltration, indicative of a highly proliferative T-cell phenotype 26 , and consistently exhibit the highest levels of AURKA expression compared to other immune cell types. These results suggested that AURKA is closely associated with the regulation of T cell proliferation within the CRC microenvironment and may serve as a key marker for identifying T proliferating cells, reflecting antitumor T-cell abundance and functional activity. Table 1 Datasets used in single cell RNA sequencing analysis Dataset Patients Cells Platform Pri/Meta PMID GSE108989 12 11,125 Smart-seq2 Primary 30479382 GSE136394 5 67,171 10X Genomics Primary, Metastatic 31484655 GSE139555 2 10,112 10X Genomics Primary 32103181 GSE146771 10 10,468 Smart-seq2 Primary 32302573 Relationship between AURKA mRNA and Tumor Immunity It has been reported that AURKA enhances CD8 + T cell infiltration and cytotoxic activity in immune-hot colorectal tumors, potentially through its negative regulation of IL-16 27,28 . To demonstrate the influence of AURKA expression on the TME, we applied multiple algorithms to characterize the immune microenvironment using TCGA CRC samples. CIBERSORT analysis showed that the high AURKA group exhibited a significant increase in the infiltration of aDC and activated memory CD4 + T cells, whereas Treg infiltration was notably decreased (Fig. 5 a–b). Additionally, ssGSEA of 28 immune cell gene sets yielded similar results, showing a significant increase in the infiltration of activated CD4 + T and memory B cells in the high AURKA expression group. In contrast, the infiltration of aDC and Tregs was significantly reduced (Fig. 5 c–d). AURKA was positively associated with the infiltration of activated CD4 + T cells but negatively associated with Tregs (Fig. 5 e). To further validate the association between AURKA expression and immune cell infiltration observed in transcriptomic analyses, we performed IHC staining on twelve pairs of CRC tissue samples (Figure S2 , Supplementary Table 1, and Supplementary Table 2). AURKA protein levels were markedly higher in CRC tissues than in adjacent normal tissues (Fig. 5 f). Notably, IHC analysis of tumor-infiltrating lymphocytes revealed increased CD4⁺ T cell infiltration in the tumor stroma relative to adjacent tissue (Fig. 5 f). Quantitative correlation analysis further demonstrated a significant positive association between AURKA and CD4⁺ IHC scores (ρ = 0.6, p < 0.05), whereas no statistically significant correlation was observed between AURKA and the proliferation marker Ki-67 (ρ = 0.37, p = 0.2) (Fig. 5 g). These IHC findings confirmed the relationship between AURKA expression and the proliferation of CD4 + T cells, indicating that AURKA may act as a modulatory molecule within the immune microenvironment. GSEA Analysis Highlighted Proliferative and Immune-Related Pathways in T proliferating cells GSEA is a computational method that determines whether a predefined set of genes shows statistically significant differences between two biological states 29 . We performed GSEA to examine the single-cell transcriptomic data to elucidate the role of T proliferating cells within the immune microenvironment of CRC. GSEA revealed that T proliferating cells were markedly enriched in cell cycle-associated pathways, including the G2/M checkpoint, E2F targets, and mitotic spindles. Additionally, these cells exhibited moderate enrichment in immune-related pathways, suggesting their dual role in promoting T cell expansion and modulating immune responses within the TME (Fig. 6 a–d). Notably, the single-cell GSEA findings were consistent with the bulk RNA sequencing analyses, further underscoring the critical involvement of T proliferating cells in driving CRC progression and shaping the local immune landscape. AURKA Positively Associates with CD4⁺ Activated Memory T Cells in Immunotherapy-Responsive Models To elucidate the immunological basis of AURKA activity during ICB therapy, its relationship with CD4⁺ activated memory T cells were examined in a CRC mouse model stratified by treatment response. Boxplot analysis showed that both AURKA expression and the proportion of CD4⁺ activated memory T cells were significantly elevated in the responder group compared to baseline and non-responder groups ( p < 0.05) (Fig. 7 a). Correlation analysis further revealed a strong positive association between AURKA expression and CD4⁺ activated memory T cell abundance in responders (R = 0.47, p = 0.0019). In contrast, this correlation was negligible in baseline (R = − 0.01, p = 0.9370) and non-responder groups (R = − 0.22, p = 0.5670) (Fig. 7 b). These results indicated that AURKA may play a critical role in enhancing antitumor immune activation, potentially by promoting the expansion or maintenance of CD4⁺ memory T cells in effective immunotherapy contexts. AURKA Expression Correlates with Divergent Survival Outcomes in Immunotherapy-Treated Patients To explore the prognostic relevance of AURKA in the context of immune checkpoint blockade (ICB), we performed survival analyses in patients treated with PD-1, PD-L1, and CTLA-4 inhibitors. Kaplan–Meier curves revealed that in the PD-1 treatment cohort, patients with high AURKA expression exhibited significantly improved overall survival (OS) (HR = 0.67, 95% CI: 0.49–0.93, p = 0.015) and better recurrence-free survival (RFS) (HR = 0.45, 95% CI: 0.33–0.61, p = 1.4×10⁻ 7 ) compared to the low-expression group (Fig. 8 . a). Similarly, in the PD-L1 cohort: elevated AURKA predicted improved OS (HR = 0.76, 95% CI: 0.59–0.97, p = 0.03) and strikingly better RFS (HR = 0.25, 95% CI: 0.16–0.39, p = 2.7×10⁻¹¹) (Fig. 8 . b). And in the CTLA-4 cohort: high AURKA was associated with enhanced OS (HR = 0.39, 95% CI: 0.20–0.78, p = 0.0059) and favorable RFS (Fig. 8 . c). Collectively, these results confirm that high AURKA expression predicts good prognosis (both OS and RFS) across patients treated with different immune checkpoint inhibitors. Discussion The emerging paradigm of AURKA as a bidirectional regulator in cancer biology–orchestrating both tumor-intrinsic proliferation and immune-extrinsic modulation–has transformative implications for CRC therapeutics. While prior studies emphasized its oncogenic functions in malignant cells 30 – 32 , our integrative multi-omics analysis reveals its previously unappreciated immunomodulatory dimension within the CRC microenvironment. By harmonizing bulk/single-cell transcriptomics, immune deconvolution, and preclinical validation, we demonstrate that AURKA operates at the nexus of cell cycle control and antitumor immunity, with context-dependent effects on clinical outcomes. Consistent with pan-cancer cohorts 31 , 33 – 39 , we confirmed significant AURKA upregulation in CRC tissues ( p < 0.05), particularly in adenocarcinomas. This overexpression aligns with its canonical role in mitotic fidelity and genomic instability 30 , 32 . However, the lack of correlation with conventional progression markers (TNM stage, nodal metastasis) suggests its oncogenic effects may be mediated through TME crosstalk rather than autonomous tumor pathways. Critically, survival analyses revealed a paradoxical association: elevated AURKA predicted improved OS (HR = 0.67, p = 0.015) and DFS (HR = 0.62), contrasting with prior reports linking tumor-cell-specific AURKA to poor prognosis 31 . This discrepancy underscores the paramount importance of cellular sourcing in biomarker interpretation. Single-cell resolution demonstrated that AURKA is predominantly expressed by T proliferating cells, which exhibit dual transcriptional signatures of proliferation (E2F targets) and immune activation (IL-2/STAT5 signaling). These data posit a counterbalancing mechanism: high global AURKA expression reflects robust infiltration of immunoprotective T proliferating cells, which may neutralize its tumor-promoting effects—mirroring immune-modulatory chemokines like CXCL11/CXCL9 40,41 . Immune landscape analyses further substantiate this model. AURKA presented a positive correlation with activated CD4⁺ memory T cells (CIBERSORT: p < 0.01; ssGSEA: p < 0.001) and negative association with immunosuppressive Tregs. And AURKA-CD4⁺ T cell coupling (r = 0.60, p < 0.05) were confirmed via IHC validation. Pathway enrichment (GSEA) indicates that AURKAhigh T proliferating cells co-express proliferative drivers (G2/M checkpoint genes) and immune effectors (cytokine signaling pathways), suggesting AURKA facilitates clonal expansion of antitumor T cells. This immune-priming function may underlie its predictive power in immunotherapy. The expression of AURKA showed strong correlation with CD4⁺memory T cells in anti-PD-1 responders (R = 0.47, p = 0.0019) as well as association with prolonged OS in PD-1/PD-L1/CTLA-4 cohorts (HR = 0.39–0.76; p < 0.05). Collectively, AURKA emerges as a pleiotropic rheostat that calibrates tumor proliferation against immune surveillance within the CRC TME. This study had some limitations. Firstly, bulk RNA-seq data cannot dissect cell-type-specific AURKA contributions. Multiplexed IHC co-staining or spatial transcriptomics is needed to resolve their cellular origins. Secondly, prognostic utility requires confirmation in standardized prospective cohorts. Thirdly, T cell-specific AURKA knockout models are essential to confirm immunomodulatory causality. Finally, in vivo studies with AURKA modulation (KO/OE) are required to confirm their immunomodulatory functions. In redefining AURKA as a dual-axis regulator of CRC biology, this study resolves the prognostic paradox through cellular contextualization. Its enrichment in T proliferating cells links high expression to enhanced antitumor immunity, countervailing tumor-intrinsic oncogenicity. The differential survival associations across ICB contexts (PD-1 > PD-L1/CTLA-4) position AURKA as a stratification biomarker for immunotherapy selection. Future efforts should prioritize in developing AURKA modulators to boost T-cell immunity, or using AURKA expression to guide personalized immunotherapy. These advances may unlock new therapeutic avenues in CRC precision immuno-oncology. Declarations Funding information This project was funded by the Ningbo Key Medical Discipline Construction Project (No.2022-B11) and Zhejiang Medical and Health Science and Technology Program (No.2024KY1555). Competing interest The authors declare no competing interests. Informed Consent All patients provided informed consent and signed the operation informed consent form. This study was approved by the Ethics Committee of Ningbo No.2 Hospital (No. YJ-NBEY-KYSB-2023-081-01). Clinical trial number Not applicable. Consent for publication All the authors confirming that written informed consent was obtained from all subjects and/or their legal guardian(s). Ethical Approval All procedures involving human colorectal cancer tissue specimens were conducted in accordance with the ethical standards of the Institutional Review Board of Ningbo No. 2 Hospital (Approval No.YJ-NBEY-KYSB-2023-081-01) and with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all human participants prior to sample collection. Data availability The bulk RNA sequencing data analyzed in this study were obtained from The Cancer Genome Atlas (TCGA) COAD cohort. These data are publicly available through the Genomic Data Commons Data Portal under accession TCGA-COAD (https://portal.gdc.cancer.gov/). Single-cell transcriptomic datasets supporting our findings have been deposited in the NCBI Gene Expression Omnibus (GEO) under the following accession numbers: GSE108989, GSE139555, GSE146771 and GSE136394. All custom scripts used for data processing and analysis are available from the corresponding author upon reasonable request. References Arnold, M. et al. Global patterns and trends in colorectal cancer incidence and mortality. Gut 66 , 683-691, doi:10.1136/gutjnl-2015-310912 (2017). Murphy, C. C. & Zaki, T. A. Changing epidemiology of colorectal cancer—birth cohort effects and emerging risk factors. 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Galetta, D. & Cortes-Dericks, L. Promising therapy in lung cancer: spotlight on aurora kinases. Cancers 12 , 3371 (2020). Goos, J. A. C. M. et al. Aurora kinase A (AURKA) expression in colorectal cancer liver metastasis is associated with poor prognosis. British Journal of Cancer 109 , 2445-2452, doi:10.1038/bjc.2013.608 (2013). He, L. et al. Identification of Aurora-A as a direct target of E2F3 during G2/M cell cycle progression. Journal of Biological Chemistry 283 , 31012-31020 (2008). Du, R., Huang, C., Liu, K., Li, X. & Dong, Z. Targeting AURKA in Cancer: molecular mechanisms and opportunities for Cancer therapy. Molecular cancer 20 , 1-27 (2021). Furukawa, T. et al. AURKA is one of the downstream targets of MAPK1/ERK2 in pancreatic cancer. Oncogene 25 , 4831-4839, doi:10.1038/sj.onc.1209494 (2006). Kivinummi, K. et al. The expression of AURKA is androgen regulated in castration-resistant prostate cancer. Scientific Reports 7 , doi:10.1038/s41598-017-18210-3 (2017). Miralaei, N., Majd, A., Ghaedi, K., Peymani, M. & Safaei, M. Integrated pan‐cancer of AURKA expression and drug sensitivity analysis reveals increased expression of AURKA is responsible for drug resistance. Cancer Medicine 10 , 6428-6441, doi:10.1002/cam4.4161 (2021). Sehdev, V. et al. HDM2 Regulation by AURKA Promotes Cell Survival in Gastric Cancer. Clinical Cancer Research 20 , 76-86, doi:10.1158/1078-0432.Ccr-13-1187 (2014). Wang, F. et al. Combination of AURKA inhibitor and HSP90 inhibitor to treat breast cancer with AURKA overexpression and TP53 mutations. Medical Oncology 39 , doi:10.1007/s12032-022-01777-x (2022). Zheng, F. et al. Nuclear AURKA acquires kinase-independent transactivating function to enhance breast cancer stem cell phenotype. Nature Communications 7 , doi:10.1038/ncomms10180 (2016). Cao, Y. et al. CXCL11 Correlates With Antitumor Immunity and an Improved Prognosis in Colon Cancer. Frontiers in Cell and Developmental Biology 9 , doi:10.3389/fcell.2021.646252 (2021). Xue, S., Su, X.-m., Ke, L.-n. & Huang, Y.-g. CXCL9 correlates with antitumor immunity and is predictive of a favorable prognosis in uterine corpus endometrial carcinoma. Frontiers in Oncology 13 , doi:10.3389/fonc.2023.1077780 (2023). Additional Declarations No competing interests reported. Supplementary Files Supfig1.tiff Fig. S1 Correlation between AURKA mRNA and clinicopathological parameters (a) Waterfall plot illustrating the top 10 mutated genes in the TCGA–CRC cohort.; (b–f) Analysis of AURKA mRNA expression in relation to nodal involvement (N stages), distant metastasis status (M stages), clinical stage (Stage I–IV), age, and sex in patients with CRC; *, p <0.05; **, p <0.01; ***, p <0.001. Supplementaryfigure2.HEandIHCstainingontwelvepairsofCRCtissuesamples.pdf Fig S2. HE and IHC staining on twelve pairs of CRC tissue samples SupplementaryTable1.ClinicalcharacteristicsofCRCpatients.xlsx Table S1. Clinical characteristics of CRC patients SupplementaryTable2.IHCscoreforAURKAKi67andCD4.xlsx Table S2. IHC score for AURKA, Ki-67, and CD4 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7001456","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491220904,"identity":"1e124f16-39d5-4e45-8570-853e8f262943","order_by":0,"name":"Yidong Xu","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Yidong","middleName":"","lastName":"Xu","suffix":""},{"id":491220905,"identity":"022ca34f-7951-4c7b-a567-ad1c4a829748","order_by":1,"name":"Wei Wang","email":"","orcid":"","institution":"Ningbo Medical Treatment Centre Lihuili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":491220906,"identity":"553bde47-66b4-48f5-b6b2-8a9e5361b88f","order_by":2,"name":"Jiazi Yu","email":"","orcid":"","institution":"Ningbo Medical Treatment Centre Lihuili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiazi","middleName":"","lastName":"Yu","suffix":""},{"id":491220907,"identity":"3d45c9be-f321-4866-ab04-011fdb320dee","order_by":3,"name":"Jianpei Zhao","email":"","orcid":"","institution":"Ningbo No.2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianpei","middleName":"","lastName":"Zhao","suffix":""},{"id":491220908,"identity":"8b58fc7d-f7e4-4991-a8de-3f15b28bfe50","order_by":4,"name":"Xiaoyu Dai","email":"","orcid":"","institution":"Ningbo No.2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Dai","suffix":""},{"id":491220909,"identity":"13c7aba5-d365-401e-8834-c6b15d050040","order_by":5,"name":"Zhongchen Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3Qv2vCQBTA8ScH6XKa9VyKf8IrgUyif0iXdwTSxdqCSwYHIZCOWQP9JwpC58KDTIGuHRxuchYE6eDgaSnUwUvHQu8LBze8D/cDwOf7g/UEgDjtlF2EQKEQbFwkOCMmQ+o/BSk6CfwgHdMg4bscKCe5ktH2MVuNyue8NrrYTyOWgDAf3l6+mMSoatZJtarvUBc4i7n7ZqBO7xcOknQLTlBNYmWJfuUeYWfBTsJf5GF3IstcomohN7klI3tKoKhB/SJaSTATsmFSH2msKIt0xfaTyfGWMOTlVmY8Dqtk3f/Ea12WzGYzH14k3+nzAWoZPzb+xYzP5/P91w5CxFV9DlwMDAAAAABJRU5ErkJggg==","orcid":"","institution":"Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Zhongchen","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-06-29 08:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7001456/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7001456/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87746672,"identity":"5e194ffe-e409-4417-a32c-96d977ce08e0","added_by":"auto","created_at":"2025-07-28 14:22:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9610689,"visible":true,"origin":"","legend":"\u003cp\u003eAURKA expression via bulk RNA sequencing in CRC\u003c/p\u003e\n\u003cp\u003e(a) Comparative analysis of AURKA mRNA expression between tumor and normal tissues across various cancer types using TCGA data; (b) Comparison of AURKA mRNA expression between tumor and adjacent normal tissues across multiple cancer types from the TCGA database; (c) AURKA mRNA expression in gastrointestinal cancer (COAD, ESCA, LIHC, and STAD) tissues and adjacent normal tissues based on TCGA data; (d) Immunohistochemical (IHC) staining of AURKA protein expression in normal and colorectal cancer (CRC) tissues; (e) AURKA mRNA expression in CRC tissues stratified based on mutation status of TP53, APC, TTN, and KRAS genes; (f) AURKA mRNA expression across different histological subtypes of CRC; (g) Association between AURKA mRNA expression and microsatellite instability status in CRC samples; (h) Correlation of AURKA mRNA expression with tumor stages in patients based on CRC; *, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; ***, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001. COAD: Colorectal Adenocarcinoma; ESCA: Esophageal Carcinoma; LIHC: Liver Hepatocellular Carcinoma; STAD: Stomach Adenocarcinoma\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/d69eec4f6a8016fbdb5a886b.png"},{"id":87746673,"identity":"b509cf17-cc71-4d0b-b2b8-4a2b6f8c3e57","added_by":"auto","created_at":"2025-07-28 14:22:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13467351,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic and prognosis in CRC\u003c/p\u003e\n\u003cp\u003e(a) Receiver operating characteristic (ROC) curve assessing the diagnostic performance of AURKA expression in CRC; (b) Kaplan–Meier analysis of overall survival in patients with CRC stratified based on AURKA expression levels; (c) Disease-free survival analysis of AURKA expression in CRC; (d) Recurrence-free survival analysis comparing high and low AURKA expression groups in CRC; (e) Progression-free survival analysis of AURKA expression in colorectal cancer. *, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; ***, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/c76b6957edaf33911ee74b03.png"},{"id":87746670,"identity":"d8c34026-eeeb-4681-9d5d-2b35e6411b58","added_by":"auto","created_at":"2025-07-28 14:22:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11580066,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expressed genes and functional enrichment analysis of AURKA mRNA in CRC\u003c/p\u003e\n\u003cp\u003e(a–c) Identification of the top 100 genes co-expressed with AURKA using the LinkedOmics database; (d–e) Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of AURKA co-expressed genes; (f–k) Gene Set Enrichment Analysis (GSEA) revealing signaling pathways associated with AURKA expression in CRC\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/8ea3a0081ecaf1b76a16afdd.png"},{"id":87748814,"identity":"160f0df5-7f27-4168-8e97-a6776e51239b","added_by":"auto","created_at":"2025-07-28 14:38:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5504748,"visible":true,"origin":"","legend":"\u003cp\u003eAURKA expression via single-cell RNA sequencing in CRC\u003c/p\u003e\n\u003cp\u003e(a–d) Representative visualizations, including cell type clustering, feature plots, and violin plots of AURKA expression, illustrating the cell type composition in individual patients from single-cell RNA sequencing datasets: (a) GSE108989, (b) GSE136394, (c) GSE139555, and (d) GSE146771\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/3333287cebe0862c59b61bc4.png"},{"id":87746676,"identity":"1ed56cad-547b-441f-991c-66112c9fbb78","added_by":"auto","created_at":"2025-07-28 14:22:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7288293,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between AURKA expression and immune cell infiltration in CRC\u003c/p\u003e\n\u003cp\u003e(a) Immune cell infiltration proportions in high and low AURKA groups analyzed using CIBERSORT; (b) Correlation between AURKA mRNA expression levels and immune cell infiltration levels using CIBERSORT; (c) Immune cell infiltration proportions in high and low AURKA groups via ssGSEA; (d) Correlation between AURKA mRNA expression levels and immune cell infiltration levels using ssGSEA. (e) Scatter plot of correlation between AURKA mRNA expression levels and immune cell (Dendritic cells, CD4+ T cells and Tregs) infiltration levels. (f)Correlation analysis between AURKA IHC scores and Ki-67 (top) or CD4 (bottom) IHC scores in CRC samples. (g) Quantification of IHC staining in tumor and adjacent tissues for AURKA, Ki-67, and CD4.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/90aee7a6d19bbe394416c6a8.png"},{"id":87746679,"identity":"73c2f1e1-2ade-44e3-a798-66e5f481a7e9","added_by":"auto","created_at":"2025-07-28 14:22:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6205261,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA analysis in T proliferating cells in different CRC datasets\u003c/p\u003e\n\u003cp\u003e(a–d) GSEA of T proliferating cells in CRC across datasets: (a) GSE108989, (b) GSE136394, (c) GSE139555, and (d) GSE146771.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/a3e02a3302c77e94dfe1c307.png"},{"id":87746681,"identity":"dc9b9f38-27f0-4f36-8c54-d87990f0c5f6","added_by":"auto","created_at":"2025-07-28 14:22:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":286315,"visible":true,"origin":"","legend":"\u003cp\u003eAURKA expression correlates with CD4⁺ activated memory T cells in immunotherapy-treated mice. (a) AURKA expression and CD4⁺ activated memory T cell proportions across baseline, responder, and non-responder groups. (b) Correlation between AURKA and CD4⁺ activated memory T cells in responders and non-responders.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/3f05e243b35dbd47f6048a40.png"},{"id":87746678,"identity":"4ca1bf9f-d1a1-4491-aadf-c1c692ea04e6","added_by":"auto","created_at":"2025-07-28 14:22:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":306879,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival analysis stratified by AURKA expression in patients receiving immune checkpoint inhibitors\u003c/p\u003e\n\u003cp\u003e(a) Overall survival (OS) (top) and recurrence-free survival (RFS) (bottom) in patients treated with PD-1 inhibitors. (b) OS (top) and RFS (bottom) in patients treated with PD-L1 inhibitors. (c) OS (top) and RFS (bottom) in patients treated with CTLA-4 inhibitors.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/de2417f1d7995e090bb8e42b.png"},{"id":90507746,"identity":"3fefb9ac-dff3-48e4-aeb0-8ce1b6a339d6","added_by":"auto","created_at":"2025-09-03 13:03:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":57919205,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/761f4c38-2f6f-4d21-8348-d79618aeede9.pdf"},{"id":87746668,"identity":"15351380-7649-4b88-8587-c50ec44925c2","added_by":"auto","created_at":"2025-07-28 14:22:55","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":284006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S1 \u003c/strong\u003eCorrelation between AURKA mRNA and clinicopathological parameters\u003c/p\u003e\n\u003cp\u003e(a) Waterfall plot illustrating the top 10 mutated genes in the TCGA–CRC cohort.; (b–f) Analysis of AURKA mRNA expression in relation to nodal involvement (N stages), distant metastasis status (M stages), clinical stage (Stage I–IV), age, and sex in patients with CRC; *, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; ***, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Supfig1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/41d2699f6ea5d223bfb5c279.tiff"},{"id":87748053,"identity":"f5fd21fd-86cd-43d7-9ba0-f821456ee498","added_by":"auto","created_at":"2025-07-28 14:30:55","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1007214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S2. \u003c/strong\u003eHE and IHC staining on twelve pairs of CRC tissue samples\u003c/p\u003e","description":"","filename":"Supplementaryfigure2.HEandIHCstainingontwelvepairsofCRCtissuesamples.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/53bba331b3b03dcce34fce21.pdf"},{"id":87746664,"identity":"0d05e145-8286-44b6-bfe9-7d996ad42c47","added_by":"auto","created_at":"2025-07-28 14:22:55","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":23013,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1. \u003c/strong\u003eClinical characteristics of CRC patients\u003c/p\u003e","description":"","filename":"SupplementaryTable1.ClinicalcharacteristicsofCRCpatients.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/34330de49e89c76498e4413f.xlsx"},{"id":87748075,"identity":"3d15a754-cd0c-4b72-8bd3-b8bc020f027c","added_by":"auto","created_at":"2025-07-28 14:30:55","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19381,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2.\u003c/strong\u003e IHC score for AURKA, Ki-67, and CD4\u003c/p\u003e","description":"","filename":"SupplementaryTable2.IHCscoreforAURKAKi67andCD4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7001456/v1/b6f90b1c38eefa5b29347f3e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Aurka orchestrates colorectal cancer progression through dual regulation of tumor proliferation and immune microenvironment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) ranks as the third most prevalent malignancy worldwide, constituting 7% of new cancer diagnoses and 11% of cancer-related mortality according to GLOBOCAN 2020 estimates\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. While surgical resection combined with chemotherapy remains the cornerstone of clinical management, the 5-year survival rate plummets from 65.1% in localized disease to 26% in metastatic presentations\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, highlighting the urgent need for innovative therapeutic strategies and reliable prognostic biomarkers.\u003c/p\u003e\u003cp\u003eEmerging evidence underscores the pivotal role of tumor microenvironment (TME) immunodynamics in carcinogenesis, in which the balance between tumor-antagonizing and tumor-promoting immune populations dictates disease progression. The antitumor armamentarium comprises CD8\u003csup\u003e+\u003c/sup\u003e cytotoxic T lymphocytes (CTLs), effector CD4\u003csup\u003e+\u003c/sup\u003e T cells, natural killer cells, and activated dendritic cells (aDCs) - the latter orchestrating CTL recruitment through CXCL9/10-CXCR3 chemokine axis activation\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. CD4\u003csup\u003e+\u003c/sup\u003e T cells potentiate antitumor immunity through two mechanisms: direct CTL activation via CD40-CD40L interactions and indirect DC licensing, which enhances tumor antigen cross-presentation\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Conversely, immunosuppressive elements, including regulatory T cells (Tregs) and myeloid-derived suppressor cells, dominate pro-tumorigenic niches\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Foxp3\u003csup\u003e+\u003c/sup\u003e Tregs maintain immune homeostasis through IL-10/TGF-β-mediated suppression yet paradoxically facilitate immune evasion by dampening CTL cytotoxicity\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAurora Kinase A (AURKA) is a critical molecular nexus in the immunological landscape. This serine/threonine kinase, localized to centrosomes and spindle microtubules, regulates mitotic fidelity through centrosome maturation and chromosomal segregation\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Beyond its canonical proliferative role, AURKA exhibits TME-modulating properties. Preclinical studies have demonstrated that AURKA inhibition paradoxically upregulates PD-L1 expression while impairing immune cell infiltration, a mechanistic conundrum potentially explaining immunotherapy resistance\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Furthermore, AURKA-driven epithelial-mesenchymal transition promotes metastatic dissemination in CRC models\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, positioning it as a multi-faceted therapeutic target.\u003c/p\u003e\u003cp\u003eIn this study, we employed an integrated multi-omics approach combining bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) with single-cell RNA-seq datasets from the TISCH2 atlas. This approach enabled systematic profiling of AURKA expression across CRC molecular subtypes and its correlation with clinicopathological determinants, including TP53 mutation status and microsatellite instability (MSI). Survival analyses revealed the prognostic value of AURKA across multiple endpoints, including overall survival (OS), disease-free survival (DFS), recurrence-free survival (RFS), and progression-free survival (PFS). The immunological functions of AURKA in immune cell infiltration were assessed using CIBERSORT, single-sample gene set enrichment analysis (ssGSEA), and immunohistochemistry (IHC). The relationship between AURKA expression and response to immune checkpoint blockade were analyzed in both human cohorts and a murine CRC immunotherapy model. These analyses revealed a positive association between AURKA expression and CD4⁺ T cell infiltration, and enhanced CD4⁺ activated memory T cell abundancein immunotherapy responders, positioning AURKA as a dual biomarker-prognostic for survival outcomes and predictive for ICB response in CRC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003ePan-Cancer Analysis Using TCGAplot\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePan-cancer transcriptional profiling was conducted using the TCGAplot R package (v8.0.0), which integrates paired/unpaired TPM-normalized expression matrices from TCGA. The analysis pipeline comprised three complementary approaches: 1) Tumor-normal comparisons across 33 cancer types were visualized through unpaired analyses using the pan_boxplot function; 2) Paired analyses restricted to 15 malignancies with ≥ 20 tumor-normal sample pairs were executed via pan_paired_boxplot; 3) Tumor-specific expression patterns across all 33 cancers were mapped using pan_tumor_boxplot. The diagnostic potential of candidate genes was quantified using ROC curve analysis (roc_curve function), with AUC values reflecting tumor-normal discrimination accuracy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSingle-Cell Analysis via TISCH2\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSingle-cell resolution analysis of AURKA expression heterogeneity was performed using the Tumor Immune Single-Cell Hub 2 (TISCH2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.comp-genomics.org\u003c/span\u003e\u003cspan address=\"http://tisch.comp-genomics.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) by integrating four CRC-specific scRNA-seq datasets. These included primary tumors, liver metastases, matched normal tissues, and peripheral blood profiles on the Smart-seq2 and 10X chromium platforms. Data pre-processing prioritized cell populations with definitive markers: malignant epithelia (EPCAM+/KRT18+), immune subsets (CD45 + T/B/myeloid cells), and stromal fibroblasts (COL1A1+/ACTA2+). Harmony batch correction enabled cross-dataset comparisons of AURKA expression (normalized transcripts/10,000 UMIs), stratified by: (1) Cellular compartment: Tumor vs. normal epithelia (GSE108989, GSE139555) and stromal-immune interactions (GSE146771); (2) Metastatic context: KRAS^(G12D)-mutant liver metastases under T-cell therapy (GSE136394); (3) Therapeutic perturbation: Myeloid-targeted responses to anti-CSF1R/CD40 agonists (GSE146771). The differential expression was assessed using the MAST framework.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMutation-Associated Expression Analysis via Tumor Immune Estimation Resource 2.0 (TIMER2)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe TIMER2 (TIMER2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to assess the association between TP53 mutations and AURKA expression in CRC. Specifically, the \"Gene_Mutation\" module was employed to compare transcriptional profiles of TP53-mutated and wild-type subgroups, with parallel analyses performed for APC, KRAS, and TTN mutations. Transcriptomic differences in AURKA expression were quantified using a two-sample Wilcoxon test, with statistical significance defined as \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. The somatic mutation status of all genes was derived from TCGA-COAD datasets using standardized mutation annotation format (MAF) files.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHistological Subtype Analysis via UALCAN\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUALCAN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to assess AURKA expression across the CRC histological subtypes (adenocarcinoma vs. mucinous adenocarcinoma) and tumor-adjacent normal tissues. To conduct this analysis, AURKA was meticulously searched in the TCGA gene panel search box. Subsequently, the \"Colon adenocarcinoma\" dataset was precisely selected from the TCGA dataset to access the dedicated expression analysis page.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Correlations and Pathway Enrichment via Biomarker Exploration of Solid Tumors (BEST) Platform\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe BEST (BEST; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rookieutopia.hiplot.com.cn\u003c/span\u003e\u003cspan address=\"https://rookieutopia.hiplot.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) platform was used to delineate AURKA's clinical relevance and functional implications of AURKA in CRC pathogenesis. By leveraging harmonized multi-omics data from TCGA, GEO, ICGC, CGGA, and ArrayExpress repositories, we conducted stratified analyses across tumor progression, TNM staging (I-IV), clinical stage (early vs. advanced), histopathological features (WHO histological subtypes), and microsatellite instability (MSI) status (MSS/MSI-H). Significant differences in AURKA expression between clinical subgroups were determined using platform-implemented ANOVA with Tukey's post-hoc test (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Functional enrichment analyses (Gene Ontology [GO], Kyoto Encyclopedia of Genes and Genomes [KEGG], and Gene Set Enrichment Analysis [GSEA]) were conducted to identify biological processes and pathways linked to AURKA dysregulation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMulti-Omics Co-expression Analysis via LinkedOmics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMulti-omics co-expression networks were constructed using LinkedOmics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.linkedomics.org\u003c/span\u003e\u003cspan address=\"http://www.linkedomics.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) by analyzing 391 TCGA colon adenocarcinoma RNA-seq profiles. The workflow included: 1) cohort selection (CRC), 2) data configuration (gene-level quantification), 3) target specification (AURKA), and 4) Spearman's correlation analysis (|r| \u0026gt;0.3, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) to identify significant interactors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurvival Analysis Using Kaplan\u003c/b\u003e–\u003cb\u003eMeier Plotter\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe clinical prognostic value of AURKA transcripts (probes: 208079_s_at, 208080_s_at, and 204092_s_at) was assessed using Kaplan–Meier Plotter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com\u003c/span\u003e\u003cspan address=\"http://kmplot.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with RNA-seq data from patients with CRC. Optimal expression cutoffs were determined using maximal survival discrimination (minimum P-value method). Log-rank tests were used to compare OS and PFS between the high and low expression groups (significance threshold: P \u0026lt; 0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmunohistochemistry (IHC) and Hematoxylin and Eosin (HE) Staining\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFormalin-fixed human colorectal cancer tissue specimens and matched adjacent peritumoral samples were collected from Ningbo No.2 Hospital between January 2022 and January 2024. Every participant signed informed consents before this study implementation. This study was approved by the Ethical Review Board of Ningbo No.2 Hospital (YJ-NBEY-KYSB-202308101). The inclusion criteria are that at least two pathologists confirmed the histopathological diagnosis is colon cancer according to the World Health Organization’s histopathological classification criteria.\u003c/p\u003e\u003cp\u003eCRC tissues and adjacent normal tissues were fixed in 4% paraformaldehyde at 4°C overnight, and then embedded in paraffin and sectioned (7-µm thickness). HE staining of the sections was performed. Immunohistochemical staining was performed using the Envision IHC kit (Maxin Biotechnologies Inc., Fuzhou, Fujian, China). Tissue sections were subjected to IHCfor AURKA(1:1000; Proteintech, China), Ki67(MXR002; Maixin Biotechnology Inc., Fuzhou, China), and CD4(MXR036; Maixin Biotechnology Inc., Fuzhou, China). Ki67 IHC was used to evaluate the proliferation of tumor cells and proliferating T cells.\u003c/p\u003e\u003cp\u003eIHC staining was evaluated semi-quantitatively by assessing both the proportion of positively stained tumor cells and the staining intensity under light microscopy. Staining intensity was classified into four grades: 0 (no staining), 1 (weak, light yellow), 2 (moderate, yellow-brown), and 3 (strong, dark brown). The percentage of positive tumor cells was scored as follows: 0 (\u0026lt; 5%), 1 (5–25%), 2 (26–50%), 3 (51–75%), and 4 (\u0026gt; 75%). A composite IHC score was calculated by multiplying the intensity score by the corresponding percentage score for each intensity category. The final score for each specimen was obtained by summing these products across all intensity levels:\u003c/p\u003e\u003cp\u003eIHC score = Σ (intensity × percentage score at that intensity).\u003c/p\u003e\u003cp\u003eThe immunohistochemical procedure was as follows. First, the slides were deparaffinized. After the antigen was recovered by high-pressure heating with citrate buffer (Maxin Bio), tissues were incubated with different antibodies at 4°C overnight and HRP-polymer secondary antibodies (Maxin Bio) for 15 min, and then incubated and developed using DAB solution (Maxin Bio).\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmunotherapy Response Analysis via TISMO database\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe TISMO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tismo.cistrome.org\u003c/span\u003e\u003cspan address=\"http://tismo.cistrome.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a publicly accessible resource that provides transcriptomic profiles of syngeneic mouse tumor models. It offers standardized analyses of gene expression, immune cell infiltration, and pathway enrichment under various immunotherapeutic conditions, including anti-PD-1, anti-PD-L1, anti-CTLA-4, and interferon treatments. All data were uniformly processed using the RIMA pipeline, which ensures high-quality control, batch effect correction, and immune cell deconvolution, enabling consistent and reliable comparisons across studies. Leveraging this resource, we systematically examined the association between AURKA expression and response to immune checkpoint blockade in murine CRC models.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical details were available in the figure legends. Unpaired/paired Student’s t test (2-tailed) were used to analyze differences between two groups. The Kaplan-Meier method was used to perform the survival curves, and the Log-rank test was used to conduct the statistical analysis. Fisher’s exact test was used for the IHC quantification of AURKA expression in COAD tissues and normal tissues. The data were shown as mean ± standard deviation (SD) unless otherwise noted. All statistical analysis were performed in GraphPad Prism 9. \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered significantly.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003emRNA Expression and Different Clinical Parameters of AURKA in CRC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first examined the differences in AURKA mRNA expression levels between human tumor tissues and their corresponding normal counterparts using the TCGAplot. The results showed that AURKA was significantly upregulated in most human cancer types, including breast cancer, colon cancer (COAD), glioblastoma, and liver cancer (LIHC), and downregulated in thyroid cancer (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;b). Although pan-cancer analysis revealed consistent AURKA overexpression across multiple malignancies, gastrointestinal cancers exhibited the most pronounced upregulation (fold change\u0026thinsp;\u0026gt;\u0026thinsp;2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec). IHC staining confirmed that AURKA expression was significantly elevated in CRC tumor tissues compared to that in adjacent non-tumorous tissues (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ed). As CRC exhibits profound genomic heterogeneity and an elevated mutation burden\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, this dysregulation may be mechanistically linked to somatic mutations within its regulatory architecture, highlighting the necessity for comprehensive mutational profiling in CRC pathogenesis research. Mutational landscape analysis of CRC samples from TCGA revealed that the most frequently mutated genes in CRC were APC (74%), TP53 (53%), KRAS (43%), and TTN (36%) (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003ea). Notably, patients with CRC harboring TP53, APC, or TTN mutations exhibit significantly altered AURKA expression compared to those without these mutations. In contrast, AURKA expression did not significantly differ between patients with and without KRAS mutations (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ee). These results suggested that frequently mutated genes contribute to the regulation of AURKA expression in CRC. Furthermore, we investigated the correlation between AURKA mRNA expression and various clinicopathological parameters, including cancer stage, sex, age, histological subtype, MSI, and nodal metastasis status. Our analysis revealed that AURKA expression was significantly higher in adenocarcinomas than in mucinous adenocarcinomas (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ef). We also found that high AURKA expression was linked to microsatellite stability (MSS), indicating that AURKA may be involved in the progression of CRC with different microsatellite statuses (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eg). The results showed no association between AURKA mRNA expression and CRC stage, sex, age, or nodal metastatic status (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eh; Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eb\u0026ndash;f). AURKA expression is associated with genomic alterations and clinical parameters, suggesting a role in the pathogenesis and progression of CRC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic and Prognosis Value of AURKA mRNA in CRC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious studies reported conflicting results regarding the relationship between AURKA expression and prognosis in CRC\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Receiver operating characteristic (ROC) analysis established a strong diagnostic capacity of AURKA in CRC, yielding an area under the curve (AUC) of 0.953 (95% CI: 0.931\u0026ndash;0.972) for distinguishing patients with high AURKA expression from controls with low AURKA expression (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). Survival analyses demonstrated significant clinical correlations: patients with elevated AURKA expression exhibited prolonged OS (median OS: 68 vs. 42 months; log-rank P\u0026thinsp;=\u0026thinsp;0.034, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb), along with improved DFS (HR\u0026thinsp;=\u0026thinsp;0.62, 95% CI 0.51\u0026ndash;0.75), RFS (HR\u0026thinsp;=\u0026thinsp;0.66, 95% CI 0.47\u0026ndash;0.72), and PFS (HR\u0026thinsp;=\u0026thinsp;0.56, 95% CI 0.42\u0026ndash;0.76) compared to the low-expression group (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec\u0026ndash;e). These multimodal findings suggested that AURKA is a dual-function biomarker for CRC diagnosis and outcome prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-expressed Genes of AURKA and Functional Enrichment Analysis in CRC from the LinkedOmics Database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LinkedOmics database (website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.linkedomics.org/login.php\u003c/span\u003e\u003c/span\u003e) was used to identify significant correlations with AURKA. This database integrates multi-omics data, including next-generation sequencing results from TCGA, focusing on gene expression and functional roles within the TME. Spearman\u0026apos;s correlation analysis showed that the genes correlated with AURKA (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). The heatmap shows 50 genes that were positively or negatively associated with AURKA (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb\u0026ndash;c). GO analyses showed that AURKA was associated with various biological processes and pathways including mitotic sister chromatid segregation, ribosome biogenesis, regulation of leukocyte migration, and leukocyte tethering or rolling (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed). AURKA was also associated with the cell cycle, DNA replication, proteasome, and ubiquitin-mediated proteolysis from the KEGG molecular pathways (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ee). We also performed a GSEA pathway enrichment analysis to explore the biological functions of AURKA. The results indicated that AURKA is primarily involved in several biological processes and pathways, including the G2/M checkpoint, DNA repair, mitotic spindle, inflammatory response, Myc target v1, and unfolded protein response (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ef\u0026ndash;k).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAURKA mRNA Expression in CRC from TISCH2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the complexity of the TME, we utilized single-cell RNA sequencing datasets, including GSE108989, GSE136394, GSE139555, GSE146771, and GSE166555 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) to comprehensively analyze its cellular composition and functional dynamics and found that AURKA was mainly expressed in T proliferating cells, many other immune cells, and tumor cells (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;d). T proliferating cells represent a subset of T lymphocytes that exhibit robust proliferative activity, typically in response to antigen stimulation, cytokine signaling, or immune activation. These cells are critical for antitumor immune responses. In the TME, T proliferating cells exhibit increased infiltration, indicative of a highly proliferative T-cell phenotype\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and consistently exhibit the highest levels of AURKA expression compared to other immune cell types. These results suggested that AURKA is closely associated with the regulation of T cell proliferation within the CRC microenvironment and may serve as a key marker for identifying T proliferating cells, reflecting antitumor T-cell abundance and functional activity.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDatasets used in single cell RNA sequencing analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDataset\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCells\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlatform\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePri/Meta\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePMID\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE108989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmart-seq2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30479382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE136394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67,171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10X Genomics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary, Metastatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31484655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE139555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10X Genomics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32103181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE146771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmart-seq2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32302573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between AURKA mRNA and Tumor Immunity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt has been reported that AURKA enhances CD8\u0026thinsp;+\u0026thinsp;T cell infiltration and cytotoxic activity in immune-hot colorectal tumors, potentially through its negative regulation of IL-16\u003csup\u003e27,28\u003c/sup\u003e. To demonstrate the influence of AURKA expression on the TME, we applied multiple algorithms to characterize the immune microenvironment using TCGA CRC samples. CIBERSORT analysis showed that the high AURKA group exhibited a significant increase in the infiltration of aDC and activated memory CD4\u003csup\u003e+\u003c/sup\u003e T cells, whereas Treg infiltration was notably decreased (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea\u0026ndash;b). Additionally, ssGSEA of 28 immune cell gene sets yielded similar results, showing a significant increase in the infiltration of activated CD4\u003csup\u003e+\u003c/sup\u003e T and memory B cells in the high AURKA expression group. In contrast, the infiltration of aDC and Tregs was significantly reduced (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec\u0026ndash;d). AURKA was positively associated with the infiltration of activated CD4\u003csup\u003e+\u003c/sup\u003e T cells but negatively associated with Tregs (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee). To further validate the association between AURKA expression and immune cell infiltration observed in transcriptomic analyses, we performed IHC staining on twelve pairs of CRC tissue samples (Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e, Supplementary Table 1, and Supplementary Table 2). AURKA protein levels were markedly higher in CRC tissues than in adjacent normal tissues (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ef). Notably, IHC analysis of tumor-infiltrating lymphocytes revealed increased CD4⁺ T cell infiltration in the tumor stroma relative to adjacent tissue (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ef). Quantitative correlation analysis further demonstrated a significant positive association between AURKA and CD4⁺ IHC scores (\u0026rho;\u0026thinsp;=\u0026thinsp;0.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas no statistically significant correlation was observed between AURKA and the proliferation marker Ki-67 (\u0026rho;\u0026thinsp;=\u0026thinsp;0.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eg). These IHC findings confirmed the relationship between AURKA expression and the proliferation of CD4\u003csup\u003e+\u003c/sup\u003e T cells, indicating that AURKA may act as a modulatory molecule within the immune microenvironment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGSEA Analysis Highlighted Proliferative and Immune-Related Pathways in T proliferating cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSEA is a computational method that determines whether a predefined set of genes shows statistically significant differences between two biological states\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. We performed GSEA to examine the single-cell transcriptomic data to elucidate the role of T proliferating cells within the immune microenvironment of CRC. GSEA revealed that T proliferating cells were markedly enriched in cell cycle-associated pathways, including the G2/M checkpoint, E2F targets, and mitotic spindles. Additionally, these cells exhibited moderate enrichment in immune-related pathways, suggesting their dual role in promoting T cell expansion and modulating immune responses within the TME (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea\u0026ndash;d). Notably, the single-cell GSEA findings were consistent with the bulk RNA sequencing analyses, further underscoring the critical involvement of T proliferating cells in driving CRC progression and shaping the local immune landscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAURKA Positively Associates with CD4⁺ Activated Memory T Cells in Immunotherapy-Responsive Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the immunological basis of AURKA activity during ICB therapy, its relationship with CD4⁺ activated memory T cells were examined in a CRC mouse model stratified by treatment response. Boxplot analysis showed that both AURKA expression and the proportion of CD4⁺ activated memory T cells were significantly elevated in the responder group compared to baseline and non-responder groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea). Correlation analysis further revealed a strong positive association between AURKA expression and CD4⁺ activated memory T cell abundance in responders (R\u0026thinsp;=\u0026thinsp;0.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0019). In contrast, this correlation was negligible in baseline (R = \u0026minus;\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9370) and non-responder groups (R = \u0026minus;\u0026thinsp;0.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5670) (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb). These results indicated that AURKA may play a critical role in enhancing antitumor immune activation, potentially by promoting the expansion or maintenance of CD4⁺ memory T cells in effective immunotherapy contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAURKA Expression Correlates with Divergent Survival Outcomes in Immunotherapy-Treated Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the prognostic relevance of AURKA in the context of immune checkpoint blockade (ICB), we performed survival analyses in patients treated with PD-1, PD-L1, and CTLA-4 inhibitors. Kaplan\u0026ndash;Meier curves revealed that in the PD-1 treatment cohort, patients with high AURKA expression exhibited significantly improved overall survival (OS) (HR\u0026thinsp;=\u0026thinsp;0.67, 95% CI: 0.49\u0026ndash;0.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and better recurrence-free survival (RFS) (HR\u0026thinsp;=\u0026thinsp;0.45, 95% CI: 0.33\u0026ndash;0.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.4\u0026times;10⁻\u003csup\u003e7\u003c/sup\u003e) compared to the low-expression group (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. a). Similarly, in the PD-L1 cohort: elevated AURKA predicted improved OS (HR\u0026thinsp;=\u0026thinsp;0.76, 95% CI: 0.59\u0026ndash;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) and strikingly better RFS (HR\u0026thinsp;=\u0026thinsp;0.25, 95% CI: 0.16\u0026ndash;0.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.7\u0026times;10⁻\u0026sup1;\u0026sup1;) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. b). And in the CTLA-4 cohort: high AURKA was associated with enhanced OS (HR\u0026thinsp;=\u0026thinsp;0.39, 95% CI: 0.20\u0026ndash;0.78, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0059) and favorable RFS (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. c). Collectively, these results confirm that high AURKA expression predicts good prognosis (both OS and RFS) across patients treated with different immune checkpoint inhibitors.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe emerging paradigm of AURKA as a bidirectional regulator in cancer biology\u0026ndash;orchestrating both tumor-intrinsic proliferation and immune-extrinsic modulation\u0026ndash;has transformative implications for CRC therapeutics. While prior studies emphasized its oncogenic functions in malignant cells\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, our integrative multi-omics analysis reveals its previously unappreciated immunomodulatory dimension within the CRC microenvironment. By harmonizing bulk/single-cell transcriptomics, immune deconvolution, and preclinical validation, we demonstrate that AURKA operates at the nexus of cell cycle control and antitumor immunity, with context-dependent effects on clinical outcomes.\u003c/p\u003e\u003cp\u003eConsistent with pan-cancer cohorts\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37 CR38\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, we confirmed significant AURKA upregulation in CRC tissues (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), particularly in adenocarcinomas. This overexpression aligns with its canonical role in mitotic fidelity and genomic instability\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. However, the lack of correlation with conventional progression markers (TNM stage, nodal metastasis) suggests its oncogenic effects may be mediated through TME crosstalk rather than autonomous tumor pathways.\u003c/p\u003e\u003cp\u003eCritically, survival analyses revealed a paradoxical association: elevated AURKA predicted improved OS (HR\u0026thinsp;=\u0026thinsp;0.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and DFS (HR\u0026thinsp;=\u0026thinsp;0.62), contrasting with prior reports linking tumor-cell-specific AURKA to poor prognosis\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This discrepancy underscores the paramount importance of cellular sourcing in biomarker interpretation. Single-cell resolution demonstrated that AURKA is predominantly expressed by T proliferating cells, which exhibit dual transcriptional signatures of proliferation (E2F targets) and immune activation (IL-2/STAT5 signaling). These data posit a counterbalancing mechanism: high global AURKA expression reflects robust infiltration of immunoprotective T proliferating cells, which may neutralize its tumor-promoting effects\u0026mdash;mirroring immune-modulatory chemokines like CXCL11/CXCL9\u003csup\u003e40,41\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eImmune landscape analyses further substantiate this model. AURKA presented a positive correlation with activated CD4⁺ memory T cells (CIBERSORT: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ssGSEA: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and negative association with immunosuppressive Tregs. And AURKA-CD4⁺ T cell coupling (r\u0026thinsp;=\u0026thinsp;0.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were confirmed via IHC validation. Pathway enrichment (GSEA) indicates that AURKAhigh T proliferating cells co-express proliferative drivers (G2/M checkpoint genes) and immune effectors (cytokine signaling pathways), suggesting AURKA facilitates clonal expansion of antitumor T cells. This immune-priming function may underlie its predictive power in immunotherapy. The expression of AURKA showed strong correlation with CD4⁺memory T cells in anti-PD-1 responders (R\u0026thinsp;=\u0026thinsp;0.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0019) as well as association with prolonged OS in PD-1/PD-L1/CTLA-4 cohorts (HR\u0026thinsp;=\u0026thinsp;0.39\u0026ndash;0.76; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Collectively, AURKA emerges as a pleiotropic rheostat that calibrates tumor proliferation against immune surveillance within the CRC TME.\u003c/p\u003e\u003cp\u003eThis study had some limitations. Firstly, bulk RNA-seq data cannot dissect cell-type-specific AURKA contributions. Multiplexed IHC co-staining or spatial transcriptomics is needed to resolve their cellular origins. Secondly, prognostic utility requires confirmation in standardized prospective cohorts. Thirdly, T cell-specific AURKA knockout models are essential to confirm immunomodulatory causality. Finally, \u003cem\u003ein vivo\u003c/em\u003e studies with AURKA modulation (KO/OE) are required to confirm their immunomodulatory functions.\u003c/p\u003e\u003cp\u003eIn redefining AURKA as a dual-axis regulator of CRC biology, this study resolves the prognostic paradox through cellular contextualization. Its enrichment in T proliferating cells links high expression to enhanced antitumor immunity, countervailing tumor-intrinsic oncogenicity. The differential survival associations across ICB contexts (PD-1\u0026thinsp;\u0026gt;\u0026thinsp;PD-L1/CTLA-4) position AURKA as a stratification biomarker for immunotherapy selection. Future efforts should prioritize in developing AURKA modulators to boost T-cell immunity, or using AURKA expression to guide personalized immunotherapy. These advances may unlock new therapeutic avenues in CRC precision immuno-oncology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by the Ningbo Key Medical Discipline Construction Project (No.2022-B11) and Zhejiang Medical and Health Science and Technology Program (No.2024KY1555).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients provided informed consent and signed the operation informed consent form. This study was approved by the Ethics Committee of Ningbo No.2 Hospital (No. YJ-NBEY-KYSB-2023-081-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors confirming that written informed consent was obtained from all subjects and/or their legal guardian(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures involving human colorectal cancer tissue specimens were conducted in accordance with the ethical standards of the Institutional Review Board of Ningbo No. 2 Hospital (Approval No.YJ-NBEY-KYSB-2023-081-01) and with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all human participants prior to sample collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bulk RNA sequencing data analyzed in this study were obtained from The Cancer Genome Atlas (TCGA) COAD cohort. These data are publicly available through the Genomic Data Commons Data Portal under accession TCGA-COAD (https://portal.gdc.cancer.gov/). Single-cell transcriptomic datasets supporting our findings have been deposited in the NCBI Gene Expression Omnibus (GEO) under the following accession numbers: GSE108989, GSE139555, GSE146771 and GSE136394. All custom scripts used for data processing and analysis are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArnold, M.\u003cem\u003e et al.\u003c/em\u003e Global patterns and trends in colorectal cancer incidence and mortality. \u003cem\u003eGut\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, 683-691, doi:10.1136/gutjnl-2015-310912 (2017).\u003c/li\u003e\n\u003cli\u003eMurphy, C. C. \u0026amp; Zaki, T. A. 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CXCL9 correlates with antitumor immunity and is predictive of a favorable prognosis in uterine corpus endometrial carcinoma. \u003cem\u003eFrontiers in Oncology\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, doi:10.3389/fonc.2023.1077780 (2023).\u003c/li\u003e\n\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":"colorectal cancer, AURKA, tumor immune microenvironment, single-cell RNA sequencing, immune cell infiltration, immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-7001456/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7001456/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The third most common malignancy globally, colorectal cancer (CRC), remains the leading cause of cancer-related mortality despite significant therapeutic advancements. Aurora Kinase A (AURKA) functions as a key molecular hub, influencing both its canonical role in cell proliferation and its modulation of the tumor microenvironment (TME). Although its dual roles in colorectal tumorigenesis remain partially characterized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We performed an integrated analysis of AURKA expression using bulk and single-cell RNA sequencing datasets in CRC. Immune cell infiltration was quantified via CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). Prognostic significance was evaluated using Kaplan–Meier survival analyses. Clinical validation employed hematoxylin and eosin (HE) staining and immunohistochemistry (IHC) on CRC specimens. The association between AURKA and immunotherapy response was further investigated using publicly available immune checkpoint blockade (ICB) cohorts and a murine CRC model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Pan-cancer analysis revealed AURKA regulation across gastrointestinal malignancies, with CRC exhibiting unique prognostic associations \u003cem\u003e(P \u003c/em\u003e\u0026lt; 0.05). Elevated AURKA expression correlated with improved survival outcomes (median OS: 68 vs. 42 months; log-rank \u003cem\u003eP\u003c/em\u003e=0.034). Pathway enrichment implicated AURKA in core cell cycle regulation (G2/M checkpoint, E2F targets) and immune-modulatory pathways (leukocyte migration, IL-2/STAT5 signaling). Validation experiment confirmed AURKA upregulation in CRC tissues and its positive correlation with CD4⁺ T-cell infiltration (transcriptomics: r = 0.62, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; IHC: r = 0.60, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). scRNA-seq resolution identified AURKA dominance in T proliferating cells. High AURKA predicted increased CD4⁺ activated memory T cells and prolonged survival in anti-PD-1 responders (HR = 0.44, \u003cem\u003eP\u003c/em\u003e = 0.003). And murine model validation demonstrated elevated AURKA in immunotherapy responders, paralleling CD4⁺ memory T-cell expansion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: AURKA serves as a dual modulator of tumor proliferation and immune engagement in CRC. Its expression reflects its role within the tumor microenvironment (TME) and T cell activity, with implications for targeting anti-PD-1 responses.\u003c/p\u003e","manuscriptTitle":"Aurka orchestrates colorectal cancer progression through dual regulation of tumor proliferation and immune microenvironment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 14:22:50","doi":"10.21203/rs.3.rs-7001456/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":"c364497d-abbb-4ae6-abfb-81767a7ee711","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-03T12:54:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 14:22:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7001456","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7001456","identity":"rs-7001456","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}