Activated Cancer-Associated Fibroblasts Correlate with Poor Survival and Decreased Lymphocyte Infiltration in Infiltrative Type Distal Cholangiocarcinoma

Activated Cancer-Associated Fibroblasts Correlate with Poor Survival and Decreased Lymphocyte Infiltration in Infiltrative Type Distal Cholangiocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Activated Cancer-Associated Fibroblasts Correlate with Poor Survival and Decreased Lymphocyte Infiltration in Infiltrative Type Distal Cholangiocarcinoma Dae Hyun Lim, Yung-Kyun Noh, Byoung Kwan Son, Dong-Hoon Kim, Kyueng-Whan Min, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5957452/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Cancer-associated fibroblasts promote tumor progression through growth facilitation, invasion, and immune evasion. This study investigated the impact of activated cancer-associated fibroblasts (aCAFs) on survival outcomes, immune response, and molecular pathways in distal bile duct (DBD) cancer. We analyzed 469 patients (418 from our cohort and 51 from The Cancer Genome Atlas) with DBD adenocarcinoma. aCAFs were evaluated using hematoxylin and eosin staining. We developed a machine learning-based survival prediction model incorporating aCAFs and clinicopathologic parameters. Additionally, we performed differential gene expression analysis, Disease Ontology analysis, gene set enrichment analysis, and in vitro drug screening of aCAFs-related genes. The presence of aCAFs significantly correlated with poor survival, advanced T and N stages, infiltrative growth pattern, lymphatic/perineural/adjacent organ invasion, and decreased tumor-infiltrating lymphocytes. aCAFs-related genes were associated with immune system functions, G protein-coupled receptor signaling, and metabolic conditions (diabetes, obesity, and abnormal C-peptide levels). In machine learning-based survival models, aCAFs emerged as a strong discriminator for survival prediction. In vitro drug screening revealed that refametinib suppressed the growth of DBD carcinoma cells expressing high levels of fibroblast activation protein-α. In conclusion, integration of machine learning and systems biology analyses identifies aCAFs as potential biomarkers for risk stratification and therapeutic targeting in DBD cancer. Biological sciences/Cancer/Gastrointestinal cancer/Biliary tract cancer/Bile duct cancer Health sciences/Oncology/Cancer/Cancer microenvironment Cancer-associated fibroblasts bile duct cancer prognosis tumor-infiltrating lymphocytes machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Cholangiocarcinoma (CCA) arises from the epithelium of the bile duct and can occur anywhere along the biliary tree and is classified into intrahepatic, perihilar, and distal types based on anatomic location. In the United States, extrahepatic CCA accounts for approximately 80% of cases, with intrahepatic CCA accounting for the remaining 20%. 1 Within extrahepatic CCA, proximal CCA, including hilar CCA, accounts for 50–60%; while distal CCA accounts for 20–30%. 2 Despite regional variations, the overall incidence of CCA has shown a consistent increase over the past decades, with different trends observed depending on the subtype. Intrahepatic CCA has shown an upward trend, while extrahepatic CCA has shown either stability or a decline in incidence rates. 3 Most patients remain asymptomatic until disease progression, resulting in delayed diagnosis, often at an advanced stage. Despite advancements in cancer biology, treatment modalities for CCA remain limited. Surgical resection stands as the sole effective intervention, feasible for only a minority of patients. 4 Notably, studies evaluating patients undergoing curative resection reveal a sobering 5-year survival rate of 32.5% for proximal CCA and a range of 20–40% for distal CCA. 5 Palliative chemotherapy constitutes the primary recourse for the majority of patients not amenable to surgery, yet the efficacy of systemic chemotherapy, including gemcitabine-based regimens, remains modest, with a median overall survival of less than 1 year and a 24-month survival rate approximating 15%. 6,7 The tumor microenvironment (TME) is a multicellular system that includes various cells that interact with tumor cells and the extracellular matrix in which these cells exist. The TME contains both cellular and non-cellular components and acts directly or indirectly on tumor cells, contributing to tumor growth and invasion. 8 Various cells such as fibroblasts, collagen, mesenchymal and endothelial cells are present in the tumor stroma or microenvironment. One of the most dominant components is cancer-associated fibroblasts (CAFs), which are spindle-shaped cells that build and remodel the ECM structure. CAFs are a heterogeneous group of cells derived from various cell lineages such as mesenchymal stem cells, hepatic stellate cells, and adipocytes. Several studies have investigated the role of CAFs in CCA. CAFs are associated with CCA growth and progression and are known to influence treatment resistance. 9 In addition, previous studies have shown that the presence of CAFs is associated with advanced clinical and histologic stage and poor prognosis in several cancers. 10 In particular, CAFs are known to contribute to drug resistance and reduce the efficacy of anticancer treatments such as chemotherapy and targeted therapies. 11 This study aimed to elucidate the association between CAFs, clinicopathologic parameters, and survival rates in patients with distal CCA. The association between immune response and CAFs expression was analyzed by assessing tumor-infiltratve lymphocytes (TILs) including CD8 + and CD4 + T cells. Functional enrichment analysis was used to explore the pathways and Disease Ontology (DO) associated with CAFs. The effect of CAFs on the survival of patients with distal bile CCA was analyzed using machine learning (ML) algorithms. Using the Genomics of Drug Sensitivity in Cancer (GDSC) database as an in vitro drug screening platform, we identified promising drug targets for CCA cell lines with elevated fibroblast activation protein-α (FAP) expression. MATERIALS AND METHODS Patient selection Our study included 418 cases diagnosed with adenocarcinoma of the distal bile duct, named distal CCA, all of whom underwent surgery between 1996 and 2019 at six institutions, with primary tumor tissue being collected. Clinicopathological parameters including age, sex, the 8th edition American Joint Committee on Cancer (AJCC) stage, tumor size, adjacent organ invasion, histopathological grade, lymphovascular invasion, perineural invasion, and surgical margin status were recorded. For survival analysis, we excluded 32 of the original 418 cases due to death within three months of surgery. Survival analysis was conducted on the remaining 386 cases. We obtained clinical data from 36 CCA cases associated with aCAFs from a total of 51 cases sourced from The Cancer Genome Atlas (TCGA) cohort (Fig. 1 ). Evaluation of aCAFs In both our cohorts and the TCGA dataset, we assessed histological slides prepared from 2–3 µm tissue sections stained with H&E. These slides were scanned to obtain digital pathology images for evaluating aCAFs. Spindle-shaped cells with large, irregularly shaped nuclei, distinct nucleoli, coarse chromatin distribution, and a relatively high nuclear-to-cytoplasmic ratio were identified as aCAFs. Inactivated cancer-associated fibroblasts were identified by their monomorphic, spindle-shaped cells with pale nuclei, inconspicuous nucleoli, and a relatively low nuclear-to-cytoplasmic ratio. Three pathologists (DHK, KWM, and MJK) independently evaluated the presence of aCAFs in representative sections from the tumor mass center or invasive front, analyzing 10 fields within intratumoral or peritumoral lesions with high cancer cell density under ×100 magnification. The criterion for identifying the presence of aCAFs was a proportion of more than 10% within a representative cancer section. 10 , 12 Exclusion criteria for the assessment of aCAFs included: (1) deposition of thick collagen bundles, (2) presence of mature fibroblasts with a spindle-shaped nucleus, (3) clustering of inflammatory cells such as TILs or neutrophils, (4) presence of immature fibroblasts in conjunction with granulation tissue surrounding tumor necrosis, and (5) nerve bundles (Fig. 2 A–L). TILs were evaluated at the tumor invasive front, with a positive classification assigned when over 200 lymphoid cells were observed within the high-power field (original magnification ×400) from our cohort. Functional enrichment analysis, in silico cytometry, and immune dysfunction For biological interpretation, the DO was analyzed, and functional similarities were investigated in 36 cases from TCGA. Gene clusters associated with aCAFs were linked to various diseases We performed an analysis to identify differentially expressed genes (DEGs) between samples with the absence and presence of aCAFs using the EnhancedVolcano tool. The cutoff criteria for DEGs were set at a 0.05 < p -value and a 0.05 0.2. A total of 532 DEGs were identified and subjected to functional enrichment analysis. This analysis was conducted using DOSE, an R/Bioconductor package for DO semantic and enrichment analysis, and DisGeNet, a discovery platform providing a comprehensive exploration of human diseases and their associated genes, including over 380,000 associations between more than 16,000 genes and 13,000 diseases. 13 Additionally, pathway analysis was performed using Reactome, a manually curated and peer-reviewed pathway database, allowing for a thorough examination of the molecular pathways associated with the presence of aCAFs. 14 In TCGA dataset, we employed in silico cytometry, known as CIBERSORT, to investigate leukocyte subsets, interferon-gamma (IFN-γ) response, and proliferation. We utilized the Tumor Immune Dysfunction and Exclusion (TIDE) tool ( http://tide.dfci.harvard.edu/)t o assess immune cell dysfunction and CD274, encoding programmed death-ligand 1 (PD-L1). Machine learing algorithms: Adaptive Elastic Net and Gradient Boosting Machine Two machine learning algorithms were employed to construct predictive survival models: the Adaptive Elastic Net (AEN), which combines linear regression with L1 and L2 penalties, and the gradient boosting machine (GBM), which is based on decision trees. 15 , 16 The AEN model was designed with the target outcome (dependent variable) defined as deceased (recurrence) or alive, along with survival time. the Gradient Boosting Machine (GBM) model used the target outcome (dependent variable) defined as deceased (recurrence) or alive. Both models incorporated common predictive variables, including age, sex, tumor size, depth of invasion, histologic grade, lymphovascular invasion, perineural invasion, adjacent organ invasion, and margin status. This was achieved by applying ML algorithms to our cohort, divided into training (70%) and validation (30%) sets. 17 The AEN is a linear regression-based model designed to improve predictive performance by balancing the lasso and ridge regularization techniques. To achieve this balance, we set the alpha value at 0.5, which allowed us to combine the strengths of both regularization methods. The optimal regularization strength was determined through 10-fold cross-validation, ensuring a balanced distribution of aCAFs across each fold for improved generalizability. The GBM model, a decision tree-based algorithm, independently selected and combined multiple covariates using multivariate Bernoulli models. To optimize model performance, hyperparameters such as the learning rate were fine-tuned using grid search cross-validation over predefined ranges. The final model was trained using the most relevant covariates identified during this process, along with the optimized hyperparameters. The predictive accuracy of the AEN model was assessed using time-dependent receiver operating characteristic (ROC) curves, while the GBM model was evaluated using standard ROC curves. The Genomics of Drug Sensitivity in Cancer database We investigated the correlation between anticancer drug sensitivity using the GDSC dataset 18 and the Cell Lines Project within the Catalog of Somatic Mutations In Cancer (COSMIC) database. 19 FAP, a classical marker expressed in aCAFs, 20 , 21 was used to investigate drug sensitivity in six CCA cell lines. The CCA cell lines were categorized into high and low FAP expression groups based on median FAP expression. Drug sensitivity was evaluated by calculating the half-maximal inhibitory concentration (IC50) across CCA cell lines stratified by FAP expression. Drug efficacy was determined through a comparative analysis of the natural logarithm of IC50 (LN IC50) values, with preferential sensitivity defined as a statistically significant negative correlation between drug response and FAP expression. Specifically, a drug was considered potentially effective if cell lines with high FAP expression demonstrated lower IC50 values compared to those with low FAP expression, indicating enhanced drug susceptibility in the context of elevated FAP expression. Statistical analysis The correlations between clinicopathological parameters and the presence of aCAFs were assessed using the χ2 test. Differences among continuous variables were examined using Student's t-test analysis. Survival curves were created through the Kaplan–Meier method and compared using the log-rank test. To identify independent prognostic markers for survival, multivariate Cox regression analyses were conducted. A two-tailed p-value of < 0.05 was considered statistically significant. All data were analyzed using R and SPSS Statistics software (version 25, IBM Corporation, Armonk, NY, USA). Ethics statements The study protocol received approval from the Institutional Review Board of Uijeongbu Eulji University Hospital (IRB number: 2024-01-017) and adhered to the ethical standards outlined in the Declaration of Helsinki, revised in 2008. Due to the retrospective nature of the study and the anonymized data used, informed consent was not obtained from individual participants. The IRB granted permission to waive the requirement for documented informed consent. RESULTS Clinicopathological parameters In our cohort of 418 patients, 240 (57%) were classified as having no aCAFs, while 178 (43%) had aCAFs. Patients with aCAFs were found to be younger and predominantly male ( p < 0.05). The presence of aCAFs was found to be associated with advanced T and N stages ( p < 0.001), infiltrative growth patterns ( p < 0.001), and a higher prevalence of lymphatic and perineural invasion ( p < 0.05). Furthermore, patients with aCAFs demonstrated a higher incidence of adjacent organ invasion, particularly affecting the pancreas and duodenum. However, no significant differences in histologic grades were observed between the two groups. The margin status did not differ significantly between the two groups (all p < 0.05) (Table 1 ). Table 1 Correlation between clinicopathological parameters and activated cancer-associated fibroblasts (aCAFs) Parameter aCAFs p - value χ 2 Absence (n = 240), n (%) Presence (n = 178), n (%) Age (years) 64.9 ± 10.1 61.3 ± 9.4 < 0.001 a Tumor size (cm) 2.4 ± 1.1 2.6 ± 1.1 0.034 Sex Male 150 (62.5%) 135 (75.8%) 0.005 Female 90 (37.5%) 43 (24.2%) T stage 1 116 (48.3%) 39 (21.9%) < 0.001 b 2 112 (46.7%) 97 (54.5%) 3 12 (5.0%) 42 (23.6%) N stage 0 < 0.001 b 1 147 (61.2%) 72 (40.4%) 2 82 (34.2%) 87 (48.9%) Growth types Papillary 22 (9.2%) 11 (6.2%) < 0.001 Nodular 49 (20.4%) 10 (5.6%) Infiltrative 169 (70.4%) 157 (88.2%) Histological grade 1 55 (22.9%) 30 (16.9%) 0.603 b 2 139 (57.9%) 119 (66.9%) 3 46 (19.2%) 29 (16.3%) Lymphatic invasion Negative 153 (63.8%) 93 (52.2%) 0.024 Positive 87 (36.2%) 85 (47.8%) Perineural invasion Negative 85 (35.4%) 41 (23.0%) 0.009 Positive 155 (64.6%) 137 (77.0%) Pancreas No invasion 126 (52.5%) 74 (41.6%) 0.035 Invasion 114 (47.5%) 104 (58.4%) Duodenum No invasion 217 (90.4%) 142 (79.8%) 0.003 Invasion 23 (9.6%) 36 (20.2%) Gallbladder No invasion 235 (97.9%) 172 (96.6%) 0.614 Invasion 5 (2.1%) 6 (3.4%) Margin status Not involved 178 (74.2%) 143 (80.3%) 0.174 Involved 62 (25.8%) 35 (19.7%) T or N stage, The 8th edition of the American Joint Committee on Cancer a Student’s t-test b Linear-by-linear association Survival analysis Among the 386 patients, those with aCAFs exhibited worse disease-free survival (DFS) and disease-specific survival (DSS) compared to patients without aCAFs (all p < 0.05). Even after adjusting for confounders such as age, T stage, N stage, histologic grade, and margin status, the statistical significance of worse DFS and DSS associated with the presence of aCAFs remained (all p < 0.05) (Table 2 ). In groups with infiltrative, papillary, and nodular growth patterns, the presence of aCAFs was associated with poorer DFS and DSS, particularly in patients with infiltrative and papillary growth patterns (all p < 0.05) (Fig. 2 M and N ). In the multivariate analysis, the presence of aCAFs was found to remain significantly associated with worse DFS and DSS in the group with an infiltrative growth pattern (all p < 0.05) (Table 3 ). Table 2 Disease-free survival and disease-specific survival analyses according to activated cancer-associated fibroblasts (aCAFs) in 386 distal bile duct cancer Disease-free survival Univariate a Multivariate b HR 95% CI aCAFs (absence vs. presence) < 0.001 65) 0.981 0.704 1.050 0.816 1.352 T stage (1 vs. 2, 3) 0.002 0.615 1.077 0.807 1.436 N stage (0 vs. 1, 2) < 0.001 < 0.001 1.785 1.375 2.319 Histological grade (1, 2 vs. 3) < 0.001 < 0.001 1.982 1.448 2.713 Margin status (negative vs. positive) 0.05 0.096 1.290 0.956 1.741 Disease-specific survival Univariate 1 Multivariate 2 HR 95% CI aCAFs (absence vs. presence) 65) 0.390 0.177 1.185 0.926 1.516 T stage (1 vs. 2, 3) < 0.001 0.073 1.281 0.977 1.68 N stage (0 vs. 1, 2) < 0.001 < 0.001 1.68 1.303 2.165 Histological grade (1, 2 vs. 3) < 0.001 < 0.001 2.007 1.483 2.716 Margin status (negative vs. positive) 0.039 0.162 1.234 0.919 1.655 a Log rank test b Cox proportional hazard model Table 3 Disease-free and disease-specific survival analyses for activated cancer-associated fibroblasts according to growth types Covariate Disease-free survival Disease-specific survival p value HR 95CI p value HR 95CI Papillary type Univariate 0.020 3.256 1.203 8.811 0.037 2.798 1.066 7.344 Multivariate 1 0.188 2.441 0.646 9.220 0.312 1.929 0.540 6.883 Nodular type Univariate 0.787 1.143 0.434 3.010 0.993 1.005 0.381 2.647 Multivariate 1 0.890 0.932 0.341 2.547 0.395 0.644 0.234 1.774 Infiltrative type Univariate < 0.001 1.668 1.266 2.197 0.011 1.412 1.082 1.842 Multivariate 1 0.001 1.697 1.257 2.292 0.048 1.330 1.002 1.766 1 Adjusted for T stage, N stage, histological grade, age and margin status Functional enrichment analysis and immune response In TCGA dataset, a comprehensive analysis using DEGs identified 532 genes associated with the presence of aCAFs. Notably, the presence of aCAFs was linked to 129 upregulated genes and 403 downregulated genes (Fig. 3 A). Subsequent gene set enrichment analysis based on pathway databases revealed associations with keratinization, innate immune processes, and G protein-coupled receptor downstream signaling pathways (Fig. 3 B). Furthermore, aCAFs-related genes exhibited significant enrichment in DO, including abnormalities such as elevated C-peptide levels, abnormal oral glucose tolerance, obesity, dyspepsia, and neuroendocrine tumors (Fig. 3 C). Hierarchical clustering of enriched DO terms unveiled associations of aCAFs-related genes with abnormalities, including elevated C-peptide, congenital hyperinsulinism, chronic inflammation, neuroendocrine tumors, and dyspepsia (Fig. 3 D). In our cohort study, the presence of aCAFs significantly correlated with a lower incidence of TILs ( p < 0.001). Additionally, analysis of TCGA dataset revealed lower expression of CD274, which encodes PD-L1, in patients with aCAFs compared to those without aCAFs ( p < 0.001). Although the presence of aCAFs exhibited trends toward increased TIDE scores, decreased IFN-γ response, and lower fractions of B cells, CD4 + T cells, CD8 + T cells, and M1 macrophages, these associations did not reach statistical significance (Fig. 3 E and F ). Drug screening and survival prediction using ML algorithms The study identified age, sex, tumor size, depth of invasion, histologic grade, lymphovascular invasion, perineural invasion, adjacent organ invasion, and margin status as significant prognostic factors incorporated into an AEN model. A nomogram was then developed using the training set (70%) and validated in validation sets (30%). In the nomogram, histologic grade showed the highest discrimination, followed by margin status and aCAFs. The mean area under the curve (AUC) for both the training set (0.660; range 0.613–0.708) and the validation set (0.658; range 0.588–0.725) in the DFS model with aCAFs exceeded that of the DFS model without aCAFs in the training set (0.638; range 0.609–0.688) and validation set (0.629; range 0.523–0.707) (Fig. 4 A). Additionally, the mean AUC for the training set (0.653; range 0.617–0.693) and validation set (0.636; range 0.563–0.716) in the DSS model with aCAFs was superior to the mean AUC for the training set (0.649; range 0.602–0.688) and validation set (0.619; range 0.534–0.701) in the DSS model without aCAFs (Fig. 4 B). In GBM, SHAP (SHapley Additive exPlanations) values identified aCAFs as the most impactful factor in survival prediction, followed by depth of invasion and tumor size. The mean AUC for the DFS model with aCAFs (0.808) demonstrated superiority over the DFS model without aCAFs (0.791) (Fig. 4 C). Similarly, the mean AUC for the DSS model with aCAFs (0.820) showed superiority over the DFS model without aCAFs (0.774). Notably, among clinicopathologic variables, aCAFs emerged as the most significant predictor of survival, closely followed by depth of invasion (Fig. 4 D). We investigated the inhibitory effects of 288 drugs on CCA cell growth with high FAP expression. Treatment with refametinib significantly inhibited CCA cell growth (all p < 0.05) (Fig. 4 E). DISCUSSION CAFs represent a phenotypically and functionally diverse group of mesenchymal lineage cells within the TME, known for their roles in promoting tumor-promoting inflammation, angiogenesis, and fibrosis. 22 Prior research has hinted at the potential significance of CAFs in CCA progression, albeit with limited mechanistic understanding. 23 Our investigation delved into the influence of aCAFs on survival outcomes in distal CCA patients. Our findings revealed a correlation between aCAFs presence and adverse DFS and DSS in distal CCA cases with infiltrative and papillary growth patterns. The absence of a survival difference in the nodular growth pattern is likely attributable to ECM degradation, as aCAFs-mediated ECM breakdown promotes tumor cell migration and invasion. 24 , 25 The infiltrative growth pattern actively degrades ECM as it progresses, whereas the nodular growth pattern shows relatively less ECM degradation. As a result, the nodular growth pattern may be less affected by aCAFs activity. Additionally, the present study established a correlation between the presence of aCAFs and advanced stages of T and N, as well as lymphatic and perineural invasion, and adjacent organ invasion. Earlier studies on CAFs in CCA have highlighted their involvement in promoting lymph node metastasis through paracrine networks. 26 Another study has associated increased alpha-smooth muscle actin expression in CCA fibroblasts with decreased 5-year survival. 27 The influence of CAFs on survival has been documented across various cancer types. Specifically, CAFs presence has been linked to reduced survival in lung adenocarcinoma and invasive ductal carcinoma of the breast. 10 , 12 Conversely, while direct investigation into CAFs presence has not occurred, desmoplasia has been correlated with favorable survival outcomes in colon cancer. 28 Although not observed in epithelial cancers, the presence of CAFs in B-cell lymphoma is associated with improved survival, sparking ongoing debate regarding the precise relationship between CAFs and clinical outcomes in different cancer types. 29 The molecular mechanisms underlying the association between cancer-associated fibroblasts (CAFs) and adverse clinicopathological outcomes remain incompletely understood. Our findings suggest that the immunosuppressive effects of aCAFs may critically impair anti-tumor immune surveillance, thereby contributing to diminished survival outcomes. CAFs are increasingly recognized as pivotal modulators of tumor progression through multifaceted mechanisms that extend beyond passive structural support. These stromal cells actively participate in complex tumor-stroma interactions by orchestrating extracellular matrix remodeling, modulating immune cell functionality, and creating a permissive microenvironment conducive to tumor growth and metastasis. 10 , 12 , 30 The pathobiological significance of CAFs is underscored by their extensive repertoire of secreted molecular mediators. These include transforming growth factor-β1, epidermal growth factor, connective tissue growth factor, and stromal cell-derived factor-1, which collectively promote tumor progression through multiple interconnected signaling pathways. The secretion of these pro-tumorigenic factors by CAFs has been demonstrated to play a pivotal role in promoting tumor cell proliferation, invasion, angiogenesis, and immune evasion. This, in turn, contributes to the transformation of the tumor microenvironment into a conducive environment for malignant progression. 31 Our findings revealed a significant association between aCAFs presence and diminished TILs within the tumor microenvironment, suggesting a potential immunosuppressive mechanism. Notably, aCAFs correlated with reduced CD274 expression, indicating that immune evasion may occur through alternative immunomodulatory pathways independent of the canonical PD-1/PD-L1 checkpoint inhibition. The observed decline in M1 macrophage populations and their associated pro-inflammatory cytokines suggests a potential mechanism for compromised cytotoxic CD8 + T cell functionality, ultimately facilitating immune escape in distal CCA. In the context of DO analysis, the observed association of aCAFs with systemic chronic inflammatory conditions, including abnormal glucose intolerance, overweight, diabetes, dyspepsia, and chronic pancreatitis, suggests the potential for a link between aCAFs and immune regulation. This relationship may contribute to the progression from chronic inflammation to localized immune suppression and fibrosis. These findings underscore the complex immunoregulatory role of aCAFs in shaping the tumor immune landscape, highlighting potential therapeutic vulnerabilities beyond conventional immune checkpoint strategies. Recently, drugs targeting immune checkpoint molecules, such as PD-L1 and cytotoxic T-lymphocyte-associated protein 4, have emerged for biliary tract cancer treatment. 32 In a phase 3 clinical trial, adding durvalumab, a PD-L1 inhibitor, to standard chemotherapy improved clinical outcomes compared to chemotherapy alone in biliary tract cancer (24-month overall survival, 24.9% vs. 10.4%). 6 Our findings on aCAFs' association with PD-L1 expression are crucial in the era of emerging immunotherapy for biliary tract cancer treatment. Targeting aCAFs may be a significant future perspective in CCA treatment, warranting further investigation. Given the promising outcomes of immunotherapy in solid tumor treatment, the development of newer immunotherapeutic agents may also yield favorable results in CCA. This study observed inhibition of high FAP-expressing CCA cell lines by refametinib, an inhibitor of MEK linked to the activation of mitogen-activated protein kinase. 33 Previous studies have implicated mitogen-activated protein kinases in fibroblast activation. 34 Further research is necessary to fully elucidate the biological mechanisms underlying this inhibition for future studies. This study had several limitations. First, its retrospective nature may entail unidentified confounding variables. While we controlled for various factors affecting survival analysis, unaccounted or omitted factors due to the limitations of retrospective data could introduce bias. Second, despite our results indicating the cancer-promoting effect of aCAFs, controversy exists regarding their role in other studies. aCAFs exhibit phenotypic and functional heterogeneity within the TME. In certain cancers, aCAFs may suppress tumor progression, and even within the same TME, they may act as both tumor promoters and suppressors 35 . Further research is necessary to elucidate aCAFs' exact role in CCA biology. Third, aCAFs were assessed solely on the basis of histological findings, which may introduce interobserver variation. To ensure validation, future studies should evaluate the presence of aCAFs using immunohistochemical staining for traditional markers such as FAP, α-smooth muscle actin, vimentin, desmin, and fibroblast-specific protein-1. Fourth, while our machine learning models demonstrated incremental improvements in survival prediction, a significant enhancement in model performance with the inclusion of aCAFs was not observed. Future studies should aim to incorporate more detailed analyses of aCAFs and clinicopathological parameters to refine prognostic models and enhance their clinical utility. Fifth, drug screening in cell lines using the GDSC has the limitation of not showing actual proliferation curves over time in cytotoxic assays. Therefore, it is necessary to verify the efficacy of candidate drugs in high-FAP cell lines through further drug testing. Sixth, although our findings suggest potential pathways and therapeutic targets linked to aCAFs, the underlying biological mechanisms remain insufficiently characterized. The validation of proteins associated with DEGs and aCAFs-related pathways through immunohistochemistry could help establish a stronger connection between genetic findings and clinical outcomes, thereby bridging the gap between molecular insights and clinical relevance. Seventh, the identification of aCAFs was based exclusively on morphological assessment utilizing H&E-stained slides. Immunohistochemical staining for markers such as FAP, α-SMA, and vimentin would be beneficial for further verification of aCAFs presence. In summary, the presence of aCAFs, linked to poor survival outcomes and diminished immune responses, may represent a pivotal biomarker for guiding therapeutic strategies, including immunotherapy and targeted treatments such as refametinib. Our comprehensive study provides novel insights into the clinical and molecular significance of aCAFs in distal CCA. By integrating histopathological assessment, bioinformatics analysis of The Cancer Genome Atlas (TCGA) dataset, and semantic ontology inference, we demonstrate a significant association between aCAFs and adverse survival outcomes. Our findings highlight the complex role of aCAFs in the tumor microenvironment, characterized by correlation with advanced cancer stages and suppression of TILs. The identification of aCAFs as a potential prognostic biomarker offers promising implications for personalized therapeutic strategies, including refined approaches to immunotherapy and targeted molecular interventions. While these results provide a critical foundation for understanding the pathobiological mechanisms of aCAFs, we acknowledge the need for further experimental validation and prospective clinical studies to translate these findings into meaningful clinical applications. Declarations Funding: This work was supported by the Medical Research Funds from Samsung Kangbuk Hospital. This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI21C1137). Yung-Kyun Noh was partly supported by NRF/MSIT (No. RS-2024-00421203) and IITP/MSIT (IITP-2021-0-02068, RS-2020-II201373, RS-2023-00220628). Author contributions The conceptualization of the study was led by KW Min. The methodology was developed by DH Lim, KW Min, and BK Son. Data curation and formal analysis were conducted by KW Min, DH Kim, SW Chae, and Y Byun. Funding for the study was acquired by SW Chae and YK Noh. The validation process was carried out by BK Son, MJ Kwon, HS Kim, JS Pyo and YB. The original draft was prepared by DH Lim and KW Min. The review and editing of the manuscript were performed by KW Min, YK Noh, DH Kim, and BK Son. All authors contributed to the writing of the paper and approved the final versions of manuscript. Data availability Public data used in this work can be acquired from TCGA Research Network portal (https://gdc.cancer.gov/about-data/publications/pancanatlas). The raw experimental data and analysis codes supporting the conclusions of this article will be made available by the corresponding author. Conflict of interest The authors declare that they have no competing interests. References Mukkamalla, S. K. R., Naseri, H. M., Kim, B. M., Katz, S. C. & Armenio, V. A. Trends in Incidence and Factors Affecting Survival of Patients With Cholangiocarcinoma in the United States. 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NEJM Evid 1 , EVIDoa2200015 (2022). https://doi.org/10.1056/EVIDoa2200015 Valle, J. et al. Cisplatin plus gemcitabine versus gemcitabine for biliary tract cancer. N Engl J Med 362 , 1273-1281 (2010). https://doi.org/10.1056/NEJMoa0908721 Wang, M. et al. Role of tumor microenvironment in tumorigenesis. J Cancer 8 , 761-773 (2017). https://doi.org/10.7150/jca.17648 Okabe, H. et al. Hepatic stellate cells may relate to progression of intrahepatic cholangiocarcinoma. Ann Surg Oncol 16 , 2555-2564 (2009). https://doi.org/10.1245/s10434-009-0568-4 Min, K. W. et al. Cancer-associated fibroblasts are associated with poor prognosis in solid type of lung adenocarcinoma in a machine learning analysis. Sci Rep 11 , 16779 (2021). https://doi.org/10.1038/s41598-021-96344-1 Garvey, C. M. et al. Anti-EGFR Therapy Induces EGF Secretion by Cancer-Associated Fibroblasts to Confer Colorectal Cancer Chemoresistance. Cancers (Basel) 12 (2020). https://doi.org/10.3390/cancers12061393 Kim, H. S. et al. Cancer-Associated Fibroblasts Together with a Decline in CD8+ T Cells Predict a Worse Prognosis for Breast Cancer Patients. Ann Surg Oncol 31 , 2114-2126 (2024). https://doi.org/10.1245/s10434-023-14715-6 Yu, G., Wang, L. G., Yan, G. R. & He, Q. Y. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31 , 608-609 (2015). https://doi.org/10.1093/bioinformatics/btu684 Yu, G. & He, Q. Y. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol Biosyst 12 , 477-479 (2016). https://doi.org/10.1039/c5mb00663e Huang, S. et al. Regularized continuous-time Markov Model via elastic net. Biometrics 74 , 1045-1054 (2018). https://doi.org/10.1111/biom.12868 Nanehkaran, Y. A. et al. The predictive model for COVID-19 pandemic plastic pollution by using deep learning method. Sci Rep 13 , 4126 (2023). https://doi.org/10.1038/s41598-023-31416-y Natekin, A. & Knoll, A. Gradient boosting machines, a tutorial. Front Neurorobot 7 , 21 (2013). https://doi.org/10.3389/fnbot.2013.00021 Yang, W. et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 41 , D955-961 (2013). https://doi.org/10.1093/nar/gks1111 Bamford, S. et al. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer 91 , 355-358 (2004). https://doi.org/10.1038/sj.bjc.6601894 Kalluri, R. The biology and function of fibroblasts in cancer. Nat Rev Cancer 16 , 582-598 (2016). https://doi.org/10.1038/nrc.2016.73 Kahounova, Z. et al. The fibroblast surface markers FAP, anti-fibroblast, and FSP are expressed by cells of epithelial origin and may be altered during epithelial-to-mesenchymal transition. Cytometry A 93 , 941-951 (2018). https://doi.org/10.1002/cyto.a.23101 Cadamuro, M. et al. The deleterious interplay between tumor epithelia and stroma in cholangiocarcinoma. Biochim Biophys Acta Mol Basis Dis 1864 , 1435-1443 (2018). https://doi.org/10.1016/j.bbadis.2017.07.028 Ravichandra, A., Bhattacharjee, S. & Affo, S. Cancer-associated fibroblasts in intrahepatic cholangiocarcinoma progression and therapeutic resistance. Adv Cancer Res 156 , 201-226 (2022). https://doi.org/10.1016/bs.acr.2022.01.009 Wetzel, M., Strickley, J., Haeberle, M. T. & Brown, T. S. Depth of Invasion of Aggressive and Nonaggressive Basal Cell Carcinoma. J Clin Aesthet Dermatol 12 , 12-14 (2019). Kraxner, A. et al. Investigating the complex interplay between fibroblast activation protein alpha-positive cancer associated fibroblasts and the tumor microenvironment in the context of cancer immunotherapy. Front Immunol 15 , 1352632 (2024). https://doi.org/10.3389/fimmu.2024.1352632 Yan, J. et al. Cancer-Associated Fibroblasts Promote Lymphatic Metastasis in Cholangiocarcinoma via the PDGF-BB/PDGFR-beta Mediated Paracrine Signaling Network. Aging Dis 15 , 369-389 (2024). https://doi.org/10.14336/AD.2023.0420 Chuaysri, C. et al. Alpha-smooth muscle actin-positive fibroblasts promote biliary cell proliferation and correlate with poor survival in cholangiocarcinoma. Oncol Rep 21 , 957-969 (2009). https://doi.org/10.3892/or_00000309 Caporale, A. et al. Is desmoplasia a protective factor for survival in patients with colorectal carcinoma? Clin Gastroenterol Hepatol 3 , 370-375 (2005). https://doi.org/10.1016/s1542-3565(04)00674-3 Haro, M. & Orsulic, S. A Paradoxical Correlation of Cancer-Associated Fibroblasts With Survival Outcomes in B-Cell Lymphomas and Carcinomas. Front Cell Dev Biol 6 , 98 (2018). https://doi.org/10.3389/fcell.2018.00098 Chakravarthy, A., Khan, L., Bensler, N. P., Bose, P. & De Carvalho, D. D. TGF-beta-associated extracellular matrix genes link cancer-associated fibroblasts to immune evasion and immunotherapy failure. Nat Commun 9 , 4692 (2018). https://doi.org/10.1038/s41467-018-06654-8 Claperon, A. et al. Hepatic myofibroblasts promote the progression of human cholangiocarcinoma through activation of epidermal growth factor receptor. Hepatology 58 , 2001-2011 (2013). https://doi.org/10.1002/hep.26585 Sabbatino, F. et al. PD-L1 and HLA Class I Antigen Expression and Clinical Course of the Disease in Intrahepatic Cholangiocarcinoma. Clin Cancer Res 22 , 470-478 (2016). https://doi.org/10.1158/1078-0432.CCR-15-0715 Gardner, A. M., Vaillancourt, R. R., Lange-Carter, C. A. & Johnson, G. L. MEK-1 phosphorylation by MEK kinase, Raf, and mitogen-activated protein kinase: analysis of phosphopeptides and regulation of activity. Mol Biol Cell 5 , 193-201 (1994). https://doi.org/10.1091/mbc.5.2.193 Chen, J. et al. Mitogen-Activated Protein Kinases Mediate Adventitial Fibroblast Activation and Neointima Formation via GATA4/Cyclin D1 Axis. Cardiovasc Drugs Ther 38 , 527-538 (2024). https://doi.org/10.1007/s10557-023-07428-1 Brechbuhl, H. M. et al. Fibroblast Subtypes Regulate Responsiveness of Luminal Breast Cancer to Estrogen. Clin Cancer Res 23 , 1710-1721 (2017). https://doi.org/10.1158/1078-0432.CCR-15-2851 Additional Declarations No competing interests reported. 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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-5957452","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440780492,"identity":"945b09e9-9ac0-4e4a-95a5-0441c148e086","order_by":0,"name":"Dae Hyun Lim","email":"","orcid":"","institution":"Uijeongbu Eulji Medical Center, Eulji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dae","middleName":"Hyun","lastName":"Lim","suffix":""},{"id":440780494,"identity":"db94bdcf-e2ca-403d-8799-44674d72afb9","order_by":1,"name":"Yung-Kyun Noh","email":"","orcid":"","institution":"Hanyang University","correspondingAuthor":false,"prefix":"","firstName":"Yung-Kyun","middleName":"","lastName":"Noh","suffix":""},{"id":440780495,"identity":"9dd76d15-08bb-4bad-8113-4771d1581f70","order_by":2,"name":"Byoung Kwan Son","email":"","orcid":"","institution":"Uijeongbu Eulji Medical Center, Eulji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Byoung","middleName":"Kwan","lastName":"Son","suffix":""},{"id":440780498,"identity":"9cfc58fe-0f90-4d41-b097-5463741213c1","order_by":3,"name":"Dong-Hoon Kim","email":"","orcid":"","institution":"Samsung Kangbuk Hospital, Sungkyunkwan University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dong-Hoon","middleName":"","lastName":"Kim","suffix":""},{"id":440780499,"identity":"bb27b9f2-99cf-4d24-a272-232d67b0a86f","order_by":4,"name":"Kyueng-Whan Min","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACCQhlk4AkxkaUljTStRwmQYvkjBzDxwW/zufxz+4x/Fzwi0Gev4Et7QM+LdISOcbGM/tuF0vcOWMsPbOPwXDGAbbDM/BpkZPIMZPm7bmd2HAjxwDIYGDcwMDejNdhQC3mv3l7ziXOv5FjDGQw2BPUAnSYGTPPjwOJG24AreP5wZC4gYHtMF4tkj3PiqV5G5ITN95IK7PmbZBInnGYLRmvFonjyRs/8/yxS5x3I3nzbZ4/Nrb97W3GeLUwCCQwMDC2gVgcBkAGMJ6Y8WtgYOA/ACT+gFjsD6CMUTAKRsEoGAWoAAAqP0hCZmRcfgAAAABJRU5ErkJggg==","orcid":"","institution":"Uijeongbu Eulji Medical Center, Eulji University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Kyueng-Whan","middleName":"","lastName":"Min","suffix":""},{"id":440780500,"identity":"405df1a1-c14b-4651-a9a9-44bd04d28947","order_by":5,"name":"Seoung Wan Chae","email":"","orcid":"","institution":"Samsung Kangbuk Hospital, Sungkyunkwan University School of 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Soo","lastName":"Pyo","suffix":""},{"id":440780504,"identity":"dc2d7d6a-accf-4736-85ca-680416b7f481","order_by":9,"name":"Yoonhyeong Byun","email":"","orcid":"","institution":"Uijeongbu Eulji Medical Center, Eulji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yoonhyeong","middleName":"","lastName":"Byun","suffix":""}],"badges":[],"createdAt":"2025-02-04 10:53:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5957452/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5957452/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-05645-2","type":"published","date":"2025-07-01T15:57:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80297517,"identity":"f68be04d-8118-49a3-ae1d-2aae6602a7fc","added_by":"auto","created_at":"2025-04-10 08:44:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":491145,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic outline of the study plan.\u003c/p\u003e","description":"","filename":"Figure1revised2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5957452/v1/9c8f1a029ac34b75b3a57487.jpg"},{"id":80295700,"identity":"3c165110-01f9-4d09-800f-37b180b0b4e0","added_by":"auto","created_at":"2025-04-10 08:36:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1703870,"visible":true,"origin":"","legend":"\u003cp\u003eHistopathological features in the tumor microenvironment of adenocarcinoma of the distal bile duct. (A) Fibroblasts crossing between cancer cells (original magnification ×200). (B and C) Activated cancer-associated fibroblasts (aCAFs) with scattered lymphoid cells (original magnification ×400). (D) Inactivated cancer-associatedfibroblasts with fibrosis (original magnification ×400). (E) Tumor-infiltrating lymphocytes (original magnification ×400). (F) Neutrophils around cancer cells (original magnification ×400). (G) Thick collagen bundles (original magnification ×400). (H) Extravasated erythrocytes around cancer cells (original magnification ×400). (I) Nerve bundles mimicking fibroblasts (original magnification ×400). (J) Spindle-shaped tumor cells mimicking fibroblasts (original magnification ×400). (K) Tumor cells with poor stroma (original magnification ×400). (L) Tumor necrosis (original magnification ×400). Survival of patients according to aCAFs in different growth types: (M) Disease-free survival in total cases (All) (p \u0026lt; 0.001), infiltrative type (p \u0026lt; 0.001), papillary type (p = 0.014), and nodular type (p = 0.73). (N) Disease-specific survival in total cases (All) (p = 0.042), infiltrative type (p = 0.013), papillary type (p = 0.029), and nodular type (p = 0.92).\u003c/p\u003e","description":"","filename":"Figure2revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5957452/v1/b389af114efa94f2d82699fe.jpg"},{"id":80295703,"identity":"558cef5f-326b-42fb-9e44-7c10a9001e9a","added_by":"auto","created_at":"2025-04-10 08:36:01","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1395220,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Gene lists associated with activated cancer-associated fibroblasts (aCAFs) identified by differentially expressed gene analysis. (B) Top gene sets associated with aCAFs identified by functional enrichment analysis. (C) Enriched disease ontology (DO) terms associated with aCAFs. (D) Tree plots illustrating DO relationships. Bar plots indicating the presence of aCAFs across different parameters: (E) low tumor-infiltrating lymphocytes (p \u0026lt; 0.001); high tumor immune dysfunction and exclusion (TIDE) score (p = 0.457); low interferon (IFN)-gamma response (p = 0.269); low memory B cells (p = 0. 418); low activated memory CD4+ T cells (p = 0.22), (F) low CD8+ T cells (p = 0.424); low M1 macrophages (p = 0.056); low CD274 (encoding PD-L1) expression (p = 0.001); high tumor cell proliferation (p = 0.171); and high fibroblast activation protein-a (FAP) expression (p = 0.504). (Error bars represent the standard error of the mean.)\u003c/p\u003e","description":"","filename":"Figure3revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5957452/v1/777e3cc829d9c40e7668827f.jpg"},{"id":80297524,"identity":"a826e814-1e98-432e-b79f-3bdbf22c455e","added_by":"auto","created_at":"2025-04-10 08:44:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1838514,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Left: Nomogram generated using the Adaptive Elastic Net (AEN) model predicting 5-year disease-free survival (DFS). Right: Time-dependent receiver operating characteristic (ROC) curves comparing the DFS model including activated cancer-associated fibroblasts (aCAFs) and the DFS model excluding aCAFs. (B) Left: Nomogram illustrating the prediction of 5-year disease-specific survival (DSS) using the AEN model. Right: Time-dependent ROC curves comparing the DSS model including aCAFs and the DSS model excluding aCAFs. (C) DFS model development using the Gradient Boosting Machine (GBM) algorithm. The figure displays the training progress on a computer screen. Supervised machine learning models were trained using the GBM algorithm, with two models evaluated: one incorporating aCAFs and the other excluding aCAFs, demonstrating their impact on DFS using standard ROC curves. (D) DSS model development using the GBM algorithm. Similar to panel C, two models were evaluated to assess the significance of aCAFs in DSS using standard ROC curves. (E) Genomics of Drug Sensitivity in Cancer (GDSC) database analysis showing Pearson correlations between refametinib's natural log half-maximal inhibitory concentration (LN IC50) values in cholangiocarcinoma cell lines and fibroblast activation protein-a (FAP) expression levels (blue, low FAP expression; red, high FAP expression). Abbreviations: activated cancer-associated fibroblasts (aCAFs); lymphovascular invasion (LVI); perineural invasion (PNI); histological grade (Grade); depth of invasion (DOI).\u003c/p\u003e","description":"","filename":"Figure4revised2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5957452/v1/5c3e966308ececab603b62df.jpg"},{"id":86178952,"identity":"08758d0e-a96e-46d4-b8de-a9df675a8bd6","added_by":"auto","created_at":"2025-07-07 16:12:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6174671,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5957452/v1/401ac69d-2f60-4110-a84c-768fd38117ab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Activated Cancer-Associated Fibroblasts Correlate with Poor Survival and Decreased Lymphocyte Infiltration in Infiltrative Type Distal Cholangiocarcinoma","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCholangiocarcinoma (CCA) arises from the epithelium of the bile duct and can occur anywhere along the biliary tree and is classified into intrahepatic, perihilar, and distal types based on anatomic location. In the United States, extrahepatic CCA accounts for approximately 80% of cases, with intrahepatic CCA accounting for the remaining 20%.\u003csup\u003e1\u003c/sup\u003e Within extrahepatic CCA, proximal CCA, including hilar CCA, accounts for 50\u0026ndash;60%; while distal CCA accounts for 20\u0026ndash;30%.\u003csup\u003e2\u003c/sup\u003e Despite regional variations, the overall incidence of CCA has shown a consistent increase over the past decades, with different trends observed depending on the subtype. Intrahepatic CCA has shown an upward trend, while extrahepatic CCA has shown either stability or a decline in incidence rates.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Most patients remain asymptomatic until disease progression, resulting in delayed diagnosis, often at an advanced stage.\u003c/p\u003e \u003cp\u003eDespite advancements in cancer biology, treatment modalities for CCA remain limited. Surgical resection stands as the sole effective intervention, feasible for only a minority of patients.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Notably, studies evaluating patients undergoing curative resection reveal a sobering 5-year survival rate of 32.5% for proximal CCA and a range of 20\u0026ndash;40% for distal CCA.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Palliative chemotherapy constitutes the primary recourse for the majority of patients not amenable to surgery, yet the efficacy of systemic chemotherapy, including gemcitabine-based regimens, remains modest, with a median overall survival of less than 1 year and a 24-month survival rate approximating 15%.\u003csup\u003e6,7\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe tumor microenvironment (TME) is a multicellular system that includes various cells that interact with tumor cells and the extracellular matrix in which these cells exist. The TME contains both cellular and non-cellular components and acts directly or indirectly on tumor cells, contributing to tumor growth and invasion.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Various cells such as fibroblasts, collagen, mesenchymal and endothelial cells are present in the tumor stroma or microenvironment. One of the most dominant components is cancer-associated fibroblasts (CAFs), which are spindle-shaped cells that build and remodel the ECM structure. CAFs are a heterogeneous group of cells derived from various cell lineages such as mesenchymal stem cells, hepatic stellate cells, and adipocytes. Several studies have investigated the role of CAFs in CCA. CAFs are associated with CCA growth and progression and are known to influence treatment resistance.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e In addition, previous studies have shown that the presence of CAFs is associated with advanced clinical and histologic stage and poor prognosis in several cancers.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e In particular, CAFs are known to contribute to drug resistance and reduce the efficacy of anticancer treatments such as chemotherapy and targeted therapies.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study aimed to elucidate the association between CAFs, clinicopathologic parameters, and survival rates in patients with distal CCA. The association between immune response and CAFs expression was analyzed by assessing tumor-infiltratve lymphocytes (TILs) including CD8\u0026thinsp;+\u0026thinsp;and CD4\u0026thinsp;+\u0026thinsp;T cells. Functional enrichment analysis was used to explore the pathways and Disease Ontology (DO) associated with CAFs. The effect of CAFs on the survival of patients with distal bile CCA was analyzed using machine learning (ML) algorithms. Using the Genomics of Drug Sensitivity in Cancer (GDSC) database as an in vitro drug screening platform, we identified promising drug targets for CCA cell lines with elevated fibroblast activation protein-α (FAP) expression.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection\u003c/h2\u003e \u003cp\u003eOur study included 418 cases diagnosed with adenocarcinoma of the distal bile duct, named distal CCA, all of whom underwent surgery between 1996 and 2019 at six institutions, with primary tumor tissue being collected. Clinicopathological parameters including age, sex, the 8th edition American Joint Committee on Cancer (AJCC) stage, tumor size, adjacent organ invasion, histopathological grade, lymphovascular invasion, perineural invasion, and surgical margin status were recorded. For survival analysis, we excluded 32 of the original 418 cases due to death within three months of surgery. Survival analysis was conducted on the remaining 386 cases. We obtained clinical data from 36 CCA cases associated with aCAFs from a total of 51 cases sourced from The Cancer Genome Atlas (TCGA) cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEvaluation of aCAFs\u003c/h3\u003e\n\u003cp\u003eIn both our cohorts and the TCGA dataset, we assessed histological slides prepared from 2\u0026ndash;3 \u0026micro;m tissue sections stained with H\u0026amp;E. These slides were scanned to obtain digital pathology images for evaluating aCAFs. Spindle-shaped cells with large, irregularly shaped nuclei, distinct nucleoli, coarse chromatin distribution, and a relatively high nuclear-to-cytoplasmic ratio were identified as aCAFs. Inactivated cancer-associated fibroblasts were identified by their monomorphic, spindle-shaped cells with pale nuclei, inconspicuous nucleoli, and a relatively low nuclear-to-cytoplasmic ratio.\u003c/p\u003e \u003cp\u003eThree pathologists (DHK, KWM, and MJK) independently evaluated the presence of aCAFs in representative sections from the tumor mass center or invasive front, analyzing 10 fields within intratumoral or peritumoral lesions with high cancer cell density under \u0026times;100 magnification. The criterion for identifying the presence of aCAFs was a proportion of more than 10% within a representative cancer section.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eExclusion criteria for the assessment of aCAFs included: (1) deposition of thick collagen bundles, (2) presence of mature fibroblasts with a spindle-shaped nucleus, (3) clustering of inflammatory cells such as TILs or neutrophils, (4) presence of immature fibroblasts in conjunction with granulation tissue surrounding tumor necrosis, and (5) nerve bundles (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;L).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTILs were evaluated at the tumor invasive front, with a positive classification assigned when over 200 lymphoid cells were observed within the high-power field (original magnification \u0026times;400) from our cohort.\u003c/p\u003e\n\u003ch3\u003eFunctional enrichment analysis, in silico cytometry, and immune dysfunction\u003c/h3\u003e\n\u003cp\u003eFor biological interpretation, the DO was analyzed, and functional similarities were investigated in 36 cases from TCGA. Gene clusters associated with aCAFs were linked to various diseases\u003c/p\u003e \u003cp\u003eWe performed an analysis to identify differentially expressed genes (DEGs) between samples with the absence and presence of aCAFs using the EnhancedVolcano tool. The cutoff criteria for DEGs were set at a 0.05\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003ep\u003c/em\u003e-value and a 0.05\u0026thinsp;\u0026lt;\u0026thinsp;false discovery rate (FDR) and | log2 fold-change (FC)| \u0026gt; 0.2. A total of 532 DEGs were identified and subjected to functional enrichment analysis. This analysis was conducted using DOSE, an R/Bioconductor package for DO semantic and enrichment analysis, and DisGeNet, a discovery platform providing a comprehensive exploration of human diseases and their associated genes, including over 380,000 associations between more than 16,000 genes and 13,000 diseases.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Additionally, pathway analysis was performed using Reactome, a manually curated and peer-reviewed pathway database, allowing for a thorough examination of the molecular pathways associated with the presence of aCAFs.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn TCGA dataset, we employed in silico cytometry, known as CIBERSORT, to investigate leukocyte subsets, interferon-gamma (IFN-γ) response, and proliferation. We utilized the Tumor Immune Dysfunction and Exclusion (TIDE) tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/)t\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/)t\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eo assess immune cell dysfunction and CD274, encoding programmed death-ligand 1 (PD-L1).\u003c/p\u003e\n\u003ch3\u003eMachine learing algorithms: Adaptive Elastic Net and Gradient Boosting Machine\u003c/h3\u003e\n\u003cp\u003eTwo machine learning algorithms were employed to construct predictive survival models: the Adaptive Elastic Net (AEN), which combines linear regression with L1 and L2 penalties, and the gradient boosting machine (GBM), which is based on decision trees.\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\u003eThe AEN model was designed with the target outcome (dependent variable) defined as deceased (recurrence) or alive, along with survival time. the Gradient Boosting Machine (GBM) model used the target outcome (dependent variable) defined as deceased (recurrence) or alive. Both models incorporated common predictive variables, including age, sex, tumor size, depth of invasion, histologic grade, lymphovascular invasion, perineural invasion, adjacent organ invasion, and margin status. This was achieved by applying ML algorithms to our cohort, divided into training (70%) and validation (30%) sets.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe AEN is a linear regression-based model designed to improve predictive performance by balancing the lasso and ridge regularization techniques. To achieve this balance, we set the alpha value at 0.5, which allowed us to combine the strengths of both regularization methods. The optimal regularization strength was determined through 10-fold cross-validation, ensuring a balanced distribution of aCAFs across each fold for improved generalizability.\u003c/p\u003e \u003cp\u003eThe GBM model, a decision tree-based algorithm, independently selected and combined multiple covariates using multivariate Bernoulli models. To optimize model performance, hyperparameters such as the learning rate were fine-tuned using grid search cross-validation over predefined ranges. The final model was trained using the most relevant covariates identified during this process, along with the optimized hyperparameters.\u003c/p\u003e \u003cp\u003eThe predictive accuracy of the AEN model was assessed using time-dependent receiver operating characteristic (ROC) curves, while the GBM model was evaluated using standard ROC curves.\u003c/p\u003e\n\u003ch3\u003eThe Genomics of Drug Sensitivity in Cancer database\u003c/h3\u003e\n\u003cp\u003eWe investigated the correlation between anticancer drug sensitivity using the GDSC dataset\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and the Cell Lines Project within the Catalog of Somatic Mutations In Cancer (COSMIC) database.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e FAP, a classical marker expressed in aCAFs,\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e was used to investigate drug sensitivity in six CCA cell lines. The CCA cell lines were categorized into high and low FAP expression groups based on median FAP expression.\u003c/p\u003e \u003cp\u003eDrug sensitivity was evaluated by calculating the half-maximal inhibitory concentration (IC50) across CCA cell lines stratified by FAP expression. Drug efficacy was determined through a comparative analysis of the natural logarithm of IC50 (LN IC50) values, with preferential sensitivity defined as a statistically significant negative correlation between drug response and FAP expression. Specifically, a drug was considered potentially effective if cell lines with high FAP expression demonstrated lower IC50 values compared to those with low FAP expression, indicating enhanced drug susceptibility in the context of elevated FAP expression.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe correlations between clinicopathological parameters and the presence of aCAFs were assessed using the χ2 test. Differences among continuous variables were examined using Student's t-test analysis. Survival curves were created through the Kaplan\u0026ndash;Meier method and compared using the log-rank test. To identify independent prognostic markers for survival, multivariate Cox regression analyses were conducted. A two-tailed p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. All data were analyzed using R and SPSS Statistics software (version 25, IBM Corporation, Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics statements\u003c/h3\u003e\n\u003cp\u003e The study protocol received approval from the Institutional Review Board of Uijeongbu Eulji University Hospital (IRB number: 2024-01-017) and adhered to the ethical standards outlined in the Declaration of Helsinki, revised in 2008. Due to the retrospective nature of the study and the anonymized data used, informed consent was not obtained from individual participants. The IRB granted permission to waive the requirement for documented informed consent.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathological parameters\u003c/h2\u003e \u003cp\u003eIn our cohort of 418 patients, 240 (57%) were classified as having no aCAFs, while 178 (43%) had aCAFs. Patients with aCAFs were found to be younger and predominantly male (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The presence of aCAFs was found to be associated with advanced T and N stages (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), infiltrative growth patterns (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a higher prevalence of lymphatic and perineural invasion (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, patients with aCAFs demonstrated a higher incidence of adjacent organ invasion, particularly affecting the pancreas and duodenum. However, no significant differences in histologic grades were observed between the two groups. The margin status did not differ significantly between the two groups (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between clinicopathological parameters and activated cancer-associated fibroblasts (aCAFs)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eaCAFs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003cem\u003e-\u003c/em\u003evalue\u003c/p\u003e \u003cp\u003eχ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsence (n\u0026thinsp;=\u0026thinsp;240), n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresence (n\u0026thinsp;=\u0026thinsp;178), n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (75.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147 (61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (40.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePapillary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfiltrative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169 (70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157 (88.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.603\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (66.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphatic invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153 (63.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerineural invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (35.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155 (64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (77.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (41.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (47.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (58.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuodenum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217 (90.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (79.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGallbladder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e235 (97.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (96.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMargin status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot involved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (74.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (80.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvolved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eT or N stage, The 8th edition of the American Joint Committee on Cancer\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eStudent\u0026rsquo;s t-test\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eLinear-by-linear association\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eAmong the 386 patients, those with aCAFs exhibited worse disease-free survival (DFS) and disease-specific survival (DSS) compared to patients without aCAFs (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Even after adjusting for confounders such as age, T stage, N stage, histologic grade, and margin status, the statistical significance of worse DFS and DSS associated with the presence of aCAFs remained (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In groups with infiltrative, papillary, and nodular growth patterns, the presence of aCAFs was associated with poorer DFS and DSS, particularly in patients with infiltrative and papillary growth patterns (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eM \u003cb\u003eand N\u003c/b\u003e). In the multivariate analysis, the presence of aCAFs was found to remain significantly associated with worse DFS and DSS in the group with an infiltrative growth pattern (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDisease-free survival and disease-specific survival analyses according to activated cancer-associated fibroblasts (aCAFs) in 386 distal bile duct cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease-free survival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultivariate\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaCAFs (absence vs. presence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026le;\u0026thinsp;65 vs. \u0026gt;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage (1 vs. 2, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage (0 vs. 1, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade (1, 2 vs. 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMargin status (negative vs. positive)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease-specific survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultivariate\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaCAFs (absence vs. presence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026le;\u0026thinsp;65 vs. \u0026gt;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage (1 vs. 2, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage (0 vs. 1, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade (1, 2 vs. 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMargin status (negative vs. positive)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eLog rank test\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eCox proportional hazard model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDisease-free and disease-specific survival analyses for activated cancer-associated fibroblasts according to growth types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eDisease-free survival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eDisease-specific survival\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e95CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePapillary type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariate\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodular type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariate\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfiltrative type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariate\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eAdjusted for T stage, N stage, histological grade, age and margin status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis and immune response\u003c/h2\u003e \u003cp\u003eIn TCGA dataset, a comprehensive analysis using DEGs identified 532 genes associated with the presence of aCAFs. Notably, the presence of aCAFs was linked to 129 upregulated genes and 403 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Subsequent gene set enrichment analysis based on pathway databases revealed associations with keratinization, innate immune processes, and G protein-coupled receptor downstream signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Furthermore, aCAFs-related genes exhibited significant enrichment in DO, including abnormalities such as elevated C-peptide levels, abnormal oral glucose tolerance, obesity, dyspepsia, and neuroendocrine tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Hierarchical clustering of enriched DO terms unveiled associations of aCAFs-related genes with abnormalities, including elevated C-peptide, congenital hyperinsulinism, chronic inflammation, neuroendocrine tumors, and dyspepsia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn our cohort study, the presence of aCAFs significantly correlated with a lower incidence of TILs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, analysis of TCGA dataset revealed lower expression of CD274, which encodes PD-L1, in patients with aCAFs compared to those without aCAFs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although the presence of aCAFs exhibited trends toward increased TIDE scores, decreased IFN-γ response, and lower fractions of B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and M1 macrophages, these associations did not reach statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE \u003cb\u003eand F\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDrug screening and survival prediction using ML algorithms\u003c/h2\u003e \u003cp\u003eThe study identified age, sex, tumor size, depth of invasion, histologic grade, lymphovascular invasion, perineural invasion, adjacent organ invasion, and margin status as significant prognostic factors incorporated into an AEN model. A nomogram was then developed using the training set (70%) and validated in validation sets (30%). In the nomogram, histologic grade showed the highest discrimination, followed by margin status and aCAFs. The mean area under the curve (AUC) for both the training set (0.660; range 0.613\u0026ndash;0.708) and the validation set (0.658; range 0.588\u0026ndash;0.725) in the DFS model with aCAFs exceeded that of the DFS model without aCAFs in the training set (0.638; range 0.609\u0026ndash;0.688) and validation set (0.629; range 0.523\u0026ndash;0.707) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Additionally, the mean AUC for the training set (0.653; range 0.617\u0026ndash;0.693) and validation set (0.636; range 0.563\u0026ndash;0.716) in the DSS model with aCAFs was superior to the mean AUC for the training set (0.649; range 0.602\u0026ndash;0.688) and validation set (0.619; range 0.534\u0026ndash;0.701) in the DSS model without aCAFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In GBM, SHAP (SHapley Additive exPlanations) values identified aCAFs as the most impactful factor in survival prediction, followed by depth of invasion and tumor size. The mean AUC for the DFS model with aCAFs (0.808) demonstrated superiority over the DFS model without aCAFs (0.791) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Similarly, the mean AUC for the DSS model with aCAFs (0.820) showed superiority over the DFS model without aCAFs (0.774). Notably, among clinicopathologic variables, aCAFs emerged as the most significant predictor of survival, closely followed by depth of invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe investigated the inhibitory effects of 288 drugs on CCA cell growth with high FAP expression. Treatment with refametinib significantly inhibited CCA cell growth (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCAFs represent a phenotypically and functionally diverse group of mesenchymal lineage cells within the TME, known for their roles in promoting tumor-promoting inflammation, angiogenesis, and fibrosis.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Prior research has hinted at the potential significance of CAFs in CCA progression, albeit with limited mechanistic understanding.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Our investigation delved into the influence of aCAFs on survival outcomes in distal CCA patients. Our findings revealed a correlation between aCAFs presence and adverse DFS and DSS in distal CCA cases with infiltrative and papillary growth patterns. The absence of a survival difference in the nodular growth pattern is likely attributable to ECM degradation, as aCAFs-mediated ECM breakdown promotes tumor cell migration and invasion.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The infiltrative growth pattern actively degrades ECM as it progresses, whereas the nodular growth pattern shows relatively less ECM degradation. As a result, the nodular growth pattern may be less affected by aCAFs activity. Additionally, the present study established a correlation between the presence of aCAFs and advanced stages of T and N, as well as lymphatic and perineural invasion, and adjacent organ invasion. Earlier studies on CAFs in CCA have highlighted their involvement in promoting lymph node metastasis through paracrine networks.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Another study has associated increased alpha-smooth muscle actin expression in CCA fibroblasts with decreased 5-year survival.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e The influence of CAFs on survival has been documented across various cancer types. Specifically, CAFs presence has been linked to reduced survival in lung adenocarcinoma and invasive ductal carcinoma of the breast.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Conversely, while direct investigation into CAFs presence has not occurred, desmoplasia has been correlated with favorable survival outcomes in colon cancer.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Although not observed in epithelial cancers, the presence of CAFs in B-cell lymphoma is associated with improved survival, sparking ongoing debate regarding the precise relationship between CAFs and clinical outcomes in different cancer types.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe molecular mechanisms underlying the association between cancer-associated fibroblasts (CAFs) and adverse clinicopathological outcomes remain incompletely understood. Our findings suggest that the immunosuppressive effects of aCAFs may critically impair anti-tumor immune surveillance, thereby contributing to diminished survival outcomes. CAFs are increasingly recognized as pivotal modulators of tumor progression through multifaceted mechanisms that extend beyond passive structural support. These stromal cells actively participate in complex tumor-stroma interactions by orchestrating extracellular matrix remodeling, modulating immune cell functionality, and creating a permissive microenvironment conducive to tumor growth and metastasis.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e The pathobiological significance of CAFs is underscored by their extensive repertoire of secreted molecular mediators. These include transforming growth factor-β1, epidermal growth factor, connective tissue growth factor, and stromal cell-derived factor-1, which collectively promote tumor progression through multiple interconnected signaling pathways. The secretion of these pro-tumorigenic factors by CAFs has been demonstrated to play a pivotal role in promoting tumor cell proliferation, invasion, angiogenesis, and immune evasion. This, in turn, contributes to the transformation of the tumor microenvironment into a conducive environment for malignant progression.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur findings revealed a significant association between aCAFs presence and diminished TILs within the tumor microenvironment, suggesting a potential immunosuppressive mechanism. Notably, aCAFs correlated with reduced CD274 expression, indicating that immune evasion may occur through alternative immunomodulatory pathways independent of the canonical PD-1/PD-L1 checkpoint inhibition. The observed decline in M1 macrophage populations and their associated pro-inflammatory cytokines suggests a potential mechanism for compromised cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell functionality, ultimately facilitating immune escape in distal CCA. In the context of DO analysis, the observed association of aCAFs with systemic chronic inflammatory conditions, including abnormal glucose intolerance, overweight, diabetes, dyspepsia, and chronic pancreatitis, suggests the potential for a link between aCAFs and immune regulation. This relationship may contribute to the progression from chronic inflammation to localized immune suppression and fibrosis. These findings underscore the complex immunoregulatory role of aCAFs in shaping the tumor immune landscape, highlighting potential therapeutic vulnerabilities beyond conventional immune checkpoint strategies.\u003c/p\u003e \u003cp\u003eRecently, drugs targeting immune checkpoint molecules, such as PD-L1 and cytotoxic T-lymphocyte-associated protein 4, have emerged for biliary tract cancer treatment.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e In a phase 3 clinical trial, adding durvalumab, a PD-L1 inhibitor, to standard chemotherapy improved clinical outcomes compared to chemotherapy alone in biliary tract cancer (24-month overall survival, 24.9% vs. 10.4%).\u003csup\u003e6\u003c/sup\u003e Our findings on aCAFs' association with PD-L1 expression are crucial in the era of emerging immunotherapy for biliary tract cancer treatment. Targeting aCAFs may be a significant future perspective in CCA treatment, warranting further investigation. Given the promising outcomes of immunotherapy in solid tumor treatment, the development of newer immunotherapeutic agents may also yield favorable results in CCA.\u003c/p\u003e \u003cp\u003eThis study observed inhibition of high FAP-expressing CCA cell lines by refametinib, an inhibitor of MEK linked to the activation of mitogen-activated protein kinase.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Previous studies have implicated mitogen-activated protein kinases in fibroblast activation.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Further research is necessary to fully elucidate the biological mechanisms underlying this inhibition for future studies.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, its retrospective nature may entail unidentified confounding variables. While we controlled for various factors affecting survival analysis, unaccounted or omitted factors due to the limitations of retrospective data could introduce bias. Second, despite our results indicating the cancer-promoting effect of aCAFs, controversy exists regarding their role in other studies. aCAFs exhibit phenotypic and functional heterogeneity within the TME. In certain cancers, aCAFs may suppress tumor progression, and even within the same TME, they may act as both tumor promoters and suppressors\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Further research is necessary to elucidate aCAFs' exact role in CCA biology. Third, aCAFs were assessed solely on the basis of histological findings, which may introduce interobserver variation. To ensure validation, future studies should evaluate the presence of aCAFs using immunohistochemical staining for traditional markers such as FAP, α-smooth muscle actin, vimentin, desmin, and fibroblast-specific protein-1. Fourth, while our machine learning models demonstrated incremental improvements in survival prediction, a significant enhancement in model performance with the inclusion of aCAFs was not observed. Future studies should aim to incorporate more detailed analyses of aCAFs and clinicopathological parameters to refine prognostic models and enhance their clinical utility. Fifth, drug screening in cell lines using the GDSC has the limitation of not showing actual proliferation curves over time in cytotoxic assays. Therefore, it is necessary to verify the efficacy of candidate drugs in high-FAP cell lines through further drug testing. Sixth, although our findings suggest potential pathways and therapeutic targets linked to aCAFs, the underlying biological mechanisms remain insufficiently characterized. The validation of proteins associated with DEGs and aCAFs-related pathways through immunohistochemistry could help establish a stronger connection between genetic findings and clinical outcomes, thereby bridging the gap between molecular insights and clinical relevance. Seventh, the identification of aCAFs was based exclusively on morphological assessment utilizing H\u0026amp;E-stained slides. Immunohistochemical staining for markers such as FAP, α-SMA, and vimentin would be beneficial for further verification of aCAFs presence.\u003c/p\u003e \u003cp\u003eIn summary, the presence of aCAFs, linked to poor survival outcomes and diminished immune responses, may represent a pivotal biomarker for guiding therapeutic strategies, including immunotherapy and targeted treatments such as refametinib. Our comprehensive study provides novel insights into the clinical and molecular significance of aCAFs in distal CCA. By integrating histopathological assessment, bioinformatics analysis of The Cancer Genome Atlas (TCGA) dataset, and semantic ontology inference, we demonstrate a significant association between aCAFs and adverse survival outcomes. Our findings highlight the complex role of aCAFs in the tumor microenvironment, characterized by correlation with advanced cancer stages and suppression of TILs. The identification of aCAFs as a potential prognostic biomarker offers promising implications for personalized therapeutic strategies, including refined approaches to immunotherapy and targeted molecular interventions. While these results provide a critical foundation for understanding the pathobiological mechanisms of aCAFs, we acknowledge the need for further experimental validation and prospective clinical studies to translate these findings into meaningful clinical applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Medical Research Funds from\u0026nbsp;Samsung\u0026nbsp;Kangbuk\u0026nbsp;Hospital.\u003c/p\u003e\n\u003cp\u003eThis research was supported by a grant of the Korea Health Technology R\u0026amp;D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health \u0026amp; Welfare, Republic of Korea (grant number: HI21C1137).\u003c/p\u003e\n\u003cp\u003eYung-Kyun Noh was partly supported by NRF/MSIT (No. RS-2024-00421203) and IITP/MSIT (IITP-2021-0-02068, RS-2020-II201373, RS-2023-00220628).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conceptualization of the study was led by KW Min. The methodology was developed by DH Lim, KW Min, and BK Son. Data curation and formal analysis were conducted by KW Min, DH Kim, SW Chae, and Y Byun. Funding for the study was acquired by SW Chae and YK Noh. The validation process was carried out by BK Son, MJ Kwon, HS Kim, JS Pyo and YB. The original draft was prepared by DH Lim and KW Min. The review and editing of the manuscript were performed by KW Min, YK Noh, DH Kim, and BK Son. All authors contributed to the writing of the paper and approved the final versions of manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublic data used in this work can be acquired from TCGA Research Network portal (https://gdc.cancer.gov/about-data/publications/pancanatlas). The raw experimental data and analysis codes supporting the conclusions of this article will be made available by the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMukkamalla, S. K. R., Naseri, H. M., Kim, B. M., Katz, S. C. \u0026amp; Armenio, V. A. Trends in Incidence and Factors Affecting Survival of Patients With Cholangiocarcinoma in the United States. \u003cem\u003eJ Natl Compr Canc Netw\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 370-376 (2018). https://doi.org/10.6004/jnccn.2017.7056\u003c/li\u003e\n\u003cli\u003eBanales, J. 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M.\u003cem\u003e et al.\u003c/em\u003e Fibroblast Subtypes Regulate Responsiveness of Luminal Breast Cancer to Estrogen. \u003cem\u003eClin Cancer Res\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 1710-1721 (2017). https://doi.org/10.1158/1078-0432.CCR-15-2851\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cancer-associated fibroblasts, bile duct cancer, prognosis, tumor-infiltrating lymphocytes, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-5957452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5957452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCancer-associated fibroblasts promote tumor progression through growth facilitation, invasion, and immune evasion. This study investigated the impact of activated cancer-associated fibroblasts (aCAFs) on survival outcomes, immune response, and molecular pathways in distal bile duct (DBD) cancer. We analyzed 469 patients (418 from our cohort and 51 from The Cancer Genome Atlas) with DBD adenocarcinoma. aCAFs were evaluated using hematoxylin and eosin staining. We developed a machine learning-based survival prediction model incorporating aCAFs and clinicopathologic parameters. Additionally, we performed differential gene expression analysis, Disease Ontology analysis, gene set enrichment analysis, and in vitro drug screening of aCAFs-related genes. The presence of aCAFs significantly correlated with poor survival, advanced T and N stages, infiltrative growth pattern, lymphatic/perineural/adjacent organ invasion, and decreased tumor-infiltrating lymphocytes. aCAFs-related genes were associated with immune system functions, G protein-coupled receptor signaling, and metabolic conditions (diabetes, obesity, and abnormal C-peptide levels). In machine learning-based survival models, aCAFs emerged as a strong discriminator for survival prediction. In vitro drug screening revealed that refametinib suppressed the growth of DBD carcinoma cells expressing high levels of fibroblast activation protein-α. In conclusion, integration of machine learning and systems biology analyses identifies aCAFs as potential biomarkers for risk stratification and therapeutic targeting in DBD cancer.\u003c/p\u003e","manuscriptTitle":"Activated Cancer-Associated Fibroblasts Correlate with Poor Survival and Decreased Lymphocyte Infiltration in Infiltrative Type Distal Cholangiocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 08:35:56","doi":"10.21203/rs.3.rs-5957452/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T06:28:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-27T15:54:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291927014345410296245725331400952909020","date":"2025-04-14T13:31:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T13:21:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218697458490164421694783845320977490573","date":"2025-04-10T03:40:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-09T17:24:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-04T04:39:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-26T15:26:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c362926a-8240-4652-944e-bf74fc14d485","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46931514,"name":"Biological sciences/Cancer/Gastrointestinal cancer/Biliary tract cancer/Bile duct cancer"},{"id":46931515,"name":"Health sciences/Oncology/Cancer/Cancer microenvironment"}],"tags":[],"updatedAt":"2025-07-07T16:01:04+00:00","versionOfRecord":{"articleIdentity":"rs-5957452","link":"https://doi.org/10.1038/s41598-025-05645-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-01 15:57:16","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-04-10 08:35:56","video":"","vorDoi":"10.1038/s41598-025-05645-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-05645-2","workflowStages":[]},"version":"v1","identity":"rs-5957452","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5957452","identity":"rs-5957452","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}