Single-cell analysis reveals that GFAP+ dedifferentiated Schwann cells promote tumor progress in PNI-positive distal cholangiocarcinoma via lactate/HMGB1 axis

Single-cell analysis reveals that GFAP+ dedifferentiated Schwann cells promote tumor progress in PNI-positive distal cholangiocarcinoma via lactate/HMGB1 axis | 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 Single-cell analysis reveals that GFAP+ dedifferentiated Schwann cells promote tumor progress in PNI-positive distal cholangiocarcinoma via lactate/HMGB1 axis Wenlong Zhai, Ziyang Zu, Chong Zhang, Jianxiang Shi, Kunlun Chen, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5240585/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2025 Read the published version in Cell Death & Disease → Version 1 posted 9 You are reading this latest preprint version Abstract Distal cholangiocarcinoma (dCCA) is a highly lethal malignancy that accounts for approximately 40% of patients with primary cholangiocarcinoma. Remarkable cellular heterogeneity and perineural invasion (PNI) are two typical features of dCCA. Deciphering the complex interplay between neoplastic and neural cells is crucial for understanding the mechanisms propelling PNI-positive dCCA progression. Herein, we conduct single-cell RNA sequencing on 24 715 cells from two pairs of PNI-positive dCCA tumors and adjacent tissues, identifying eight unique cell types. Malignant cells exhibit significant inter- and intra-tumor heterogeneity. We delineate the compositional and functional phenotypes of five Schwann cell (SC) subsets in PNI-positive dCCA. Moreover, our analyses reveal two potential cell subtypes critical to forming PNI: NEAT1 + malignant cells characterized by hypoxia and GFAP + dedifferentiated SCs featuring hypermetabolism. Further bioinformatics uncover extensive cellular interactions between these two subpopulations. Functional experiments confirm that lactate in the hypoxic tumor microenvironment can induce GFAP -dedifferentiation in SCs, which promotes cancer cell invasion and progression through upregulating HMGB1 . Taken together, our findings offer a thorough characterization of the transcriptional profile in PNI-positive dCCA and unveil potential therapeutic targets of dCCA PNI. Biological sciences/Cancer/Cancer microenvironment Biological sciences/Cell biology/Mechanisms of disease Biological sciences/Cancer/Tumour heterogeneity distal cholangiocarcinoma dedifferentiated Schwann cells perineural invasion Lactate HMGB1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cholangiocarcinoma (CCA) represents the most prevalent malignancy of the biliary system ( 1 ). Depending on its anatomical location, CCA is typically categorized into intrahepatic cholangiocarcinoma, periportal cholangiocarcinoma, and distal cholangiocarcinoma (dCCA) ( 2 , 3 ), with the latter localized to the common bile duct below the cystic duct insertion ( 2 ). Over nearly three decades, the diagnostic morbidity and mortality of dCCA have continued to increase ( 4 ). Due to early asymptomatic or nonspecific symptoms, many cases of dCCA are diagnosed in advanced stages, leading to limited available therapeutic options and an extremely poor prognosis. Surgical resection followed by adjuvant therapy may improve survival outcomes in patients with dCCA, but the high recurrence rate after dCCA surgery remains a challenge in clinical management ( 5 ). Perineural invasion (PNI), refers to the characteristic biological process by which cancer cells invade nerves and spread along the perineurium ( 6 ). In a recent cohort study, PNI was a common pathological phenomenon, present in 81.8% of dCCA cases ( 7 ). Importantly, PNI has been recognized as a significant prognostic indicator affecting patients with resectable dCCA ( 8 , 9 ). Peripheral nerves constitute a complicated tumor ecosystem comprising diverse cell populations, including Schwann cells (SCs), and various factors. Physical contact between malignant cells and SCs has been found to promote directed movement and invasion of cancer cells ( 10 ). Fuji-Nishimura et al . demonstrated that SCs facilitate colonization of pancreatic cancer in nerves by activating the epithelial-mesenchymal transition (EMT) pathway in tumor cells ( 11 ). Recent research has illuminated that SCs could contribute to tumor progression by transitioning to a dedifferentiated state, analogous to their response to neurotrauma ( 10 ). This reprogramming of SCs leads to the re-expression of glial fibrillary acidic protein ( GFAP ), neural cell adhesion molecule 1 ( NCAM1 ), and L1 cell adhesion molecule ( L1CAM ), which can drive the development of PNI ( 10 , 12 , 13 ). Presently, the initiator and tumor-promoting effect of dedifferentiated Schwann cells (dSCs) in PNI-positive dCCA remain unclear. Consequently, a thorough comprehension of the cellular and molecular mechanism underlying neuromodulation of cancer progression is crucial for developing strategies for inhibiting tumor progression ( 6 ). Herein, we employed the powerful technique of single-cell sequencing to profile PNI-positive dCCA and adjacent tissues, and identified two PNI-associated cellular components: NEAT1 + malignant cells and GFAP + dSCs. We provided hitherto undocumented evidence that lactate in tumor hypoxic microenvironment could initiate GFAP -dedifferentiation in SCs, and the latter enhanced dCCA tumor progression through upregulating high mobility group box 1 ( HMGB1 ). Taken together, our findings offer an exhaustive transcriptomic overview and elucidate the intercellular interaction between malignant cells and SCs in dCCA, revealing potential therapeutic vulnerabilities in PNI-positive dCCA. Results Single-cell Transcriptomic Profiling Uncovered the Spectrum of Cell Populations in Human PNI-positive dCCAs To comprehensively understand the tumor ecosystem in dCCA with PNI, we conducted single-cell RNA sequencing (scRNA-seq) on tumor and matched adjacent non-neoplastic tissues from two untreated PNI-positive dCCAs (Fig. 1 A). Detailed clinicopathological features of the study population are listed in Table S1 . Following quality control and filtering, single-cell transcriptome profiles were obtained for 24 715 cells. Eight primary cell types were determined informed by established marker genes, including epithelial cells (2 696, 10.9%), myeloid cells (4 212, 17.0%), lymphoid cells (13 161, 53.3%), endothelial cells (1 946, 7.9%), Schwann cells (SCs, 472, 1.9%), fibroblast (1 919, 45.6%), MKI67 + cells (227, 0.9%), and smooth muscle cells (SMCs, 82, 0.3%, Fig. 1 B). Subsequently, we extracted all epithelial cells and identified 13 subclusters through reclustering analysis. Clusters 2 and 4 were considered normal epithelium and served as a normal reference for copy number variation (CNV) analysis due to their predominant distribution in adjacent noncancerous tissues (Fig. 1 C and Figure S1 A). A total of 1 203 malignant cells expressing high levels of KRT19 were inferred and further reclustered (Figure S1 B). Figure 1 D illustrates the initial 21 cell cluster sources for all cells. Consistent with previous dCCA studies ( 14 , 15 ), non-malignant cells (excluding SMCs) exhibited inter- and intratumoral heterogeneity across different tissues. For instance, endothelial cells, epithelial cells, myeloid cells, and fibroblasts were heavily infiltrated in tumors, whereas lymphoid cells and SCs were predominantly found in adjacent biliary ductal tissues (Fig. 1 E). Moreover, to validate our findings, we deconvoluted bulk RNA-seq data from a larger cohort of paired CHOL and normal samples using CIBERSORTx ( 16 ) to deconvolute bulk RNA-seq data from a broader cohort of paired cholangiocarcinoma (CHOL) and normal samples. The relative abundance of endothelial cells, fibroblasts, and SCs in our samples aligned with estimates from the TCGA-CHOL dataset. However, epithelial cells and immune cells displayed discrepant patterns (Figure S1 C). Highly heterogeneous hypoxic patterns of malignant cells and their contribution to the PNI-positive dCCA microenvironment To characterize the tumor cell landscape in PNI-positive dCCA, malignant cells were subsequently clustered and divided into three primary subclusters (Fig. 2 A-B). In alignment with previous findings in distal cholangiocarcinoma ( 14 ), malignant cells exhibited significant intra- and inter-tumor heterogeneity (Fig. 2 A). The distinctive gene expression patterns within these three subpopulations are illustrated in Fig. 2 B. Cluster 0 was enriched for cells that highly expressed genes in the S100 family, such as S100A4 , S100A10 , and S100A11 . S100 protein family members have been commonly observed to be dysregulated in various tumors, including intrahepatic cholangiocarcinoma, and are critically implicated in carcinogenesis and cancer progression ( 17 – 19 ). Cluster 1 was characterized by a prominent upregulation of NEAT1 and MALAT1 . These two adjacent long non-coding RNA (lncRNA) genes have been extensively documented to be involved not only in activating multiple oncogenic mechanisms but also in conferring resistance to chemotherapeutics ( 20 , 21 ). TOP2A and PTTG1 , both of which were the marker genes of Cluster 2, have been previously implicated as oncogenes in a wide variety of human tumors ( 22 , 23 ). Hypoxia is a ubiquitous property of most solid cancers and is strongly linked to tumor metastasis and invasion ( 24 ). We subsequently visualized the hypoxia statuses of malignant cells using cellular hypoxia predicting framework (CHPF) ( 25 ). Among these, most hypoxic cells were concentrated in cluster 1 ( NEAT1 + ) malignant cells, with fewer found in cluster 0 ( S100A4 + ) and cluster 2 ( TOP2A + ) (Fig. 2 C). Additionally, to explore the influence of hypoxia on the evolutionary dynamics of malignant cells in PNI-positive dCCA, Monocle2, and CytoTRACE were employed to perform unsupervised cell trajectory analysis, both of which revealed a similar differentiation pathway of malignant cells originating from hypoxic cells (Fig. 2 D, Figure S2 A), consistent with the conclusion drawn by Zhang et al . in glioblastoma ( 25 ). Three cell states (S1-S3) were defined for pseudotime trajectory analysis based on Monocle2 (Fig. 2 D). In terms of cellular status, hypoxic cells were primarily confined to S1 and S2 at the initial stage of differentiation, whereas hypoxic cells were predominantly concentrated in S3. Regarding cell clusters, NEAT1 + malignant cells (cluster 1) dominated the S1 state, appearing at the earliest stage of pseudotime and exhibiting significantly higher stemness scores. We postulated that the high stemness of cluster 1 might be related to the hypoxic induction it faced, according to previous studies ( 26 – 28 ). Correspondingly, S100A4 + malignant cells (cluster 0) constituted the primary subcluster of the S3 state and were exclusively observed in the final stage of cell differentiation. Notably, TOP2A + malignant cells (cluster 2) spanned across both S2 and S3 states, suggesting the presence of two distinct cell substates within cluster 2 (Fig. 2 D). Taken together, these findings indicated an orchestrated differentiation process of dCCA cells during PNI. PEAK1 , a novel human pseudokinase, has recently been implicated in cancer pathogenesis ( 29 ). We observed that PEAK1 + malignant cells were positioned at the beginning of the major branch and aligned well with cluster 1. Similarly, metastasis scores and hepatic vascular invasion scores were predominantly observed at the onset of differentiation. These findings suggested that cluster 1 might represent a key cell type with high invasiveness in PNI-positive dCCA. To delve deeper into the relationship between hypoxia and the malignant properties of PNI-positive dCCA, hypoxia-related signature genes from the CancerSEA database ( 30 ) and several hallmark gene sets, including EMT, IL2/STAT5, PI3K/AKT/mTOR, and KRAS signaling from the Molecular Signature Database (MSigDB), were manually curated (Table S2 ). Given gene set variation analysis (GSVA) to determine the activity score of each malignant cell, a significant positive correlation between hypoxia and the number of oncogenic pathway activities in PNI-positive dCCA was implied. The results showed a significant positive correlation between hypoxia and the number of oncogenic pathway activities in PNI-positive dCCA (Figure S2 B). Utilizing the previously inferred single-cell CNV spectrum, we observed that clusters 1 and 2 exhibited higher CNV levels than cluster 0 (Fig. 2 E). In addition, the extent of CNV accumulation was correlated with the hypoxic status of cells. As exemplified by malignant cells derived from patient 01, hypoxic malignant cells displayed significantly higher CNV levels than normoxic malignant cells, indicative of a more malignant phenotype. In this respect, high-frequency CNV events were enriched in certain chromosomes, such as chr6, chr12, and chr15 (Fig. 2 F). We categorized all malignant cells into low and high groups based on CNV levels (Fig. 2 D-E). The percentage of cells with a high degree of hypoxia within each malignant cell cluster demonstrated a positive correlation with the percentage of CNV-high cells in that cluster (Fig. 2 G). In addition, pathway enrichment analysis using GSVA revealed that MTORC1 signaling, MYC targets, E2F targets, and EMT pathways were enriched in the CNV-high group (Figure S2 C). Overall, hypoxia and high CNV levels might be crucial for preserving the malignant characteristics of cluster 1. To summarize the transcriptomic features of malignant cells, we integrated meta-information regarding cell cluster, hypoxic state, CNV status, and predicted trajectories. The dynamic gene expression patterns observed divided the cellular developmental process into two distinct phases (Fig. 2 H). Correspondingly, the initial phase primarily comprised states S1 and S2, and there was a propensity for cluster 1 cells to transition to cluster 2 during this stage. This transition was accompanied by upregulation of the hypoxia-induced gene VEGFA and the oncogenic driver AKT , as well as enhanced signaling pathways associated with hypoxia response and epithelial cell migration. In contrast, cluster 1 cells in the second phase exhibited a greater propensity to transform into cluster 0 and subsequently progress toward the S3 state, characterized by heightened expression of RPS15 and a shift in energy metabolism towards aerobic respiration (Fig. 2 H). To investigate the clinical implication of the malignant cell subtypes identified in our study, we estimated the proportion of epithelial cell subpopulations (including normal epithelial cells) within patient samples from the TCGA-CHOL cohort utilizing CIBERSORTx (Table S3). Only the abundance of cluster 1 malignant cells showed a significant correlation with reduced overall survival (OS; Fig. 2 I-J). We subsequently obtained similar results using GSE107943 as a validation dataset (Fig. 2 D, Table S4). Furthermore, utilizing information on samples from the TCGA cohort containing patient PNI status, we discovered that cluster 1 malignant cells were significantly more abundant in CHOL with PNI than CHOL without PNI (Fig. 2 K). These findings indicated that cluster 1 ( NEAT1 + ) malignant cells, characterized by hypoxia and high copy number variations, are associated with PNI progression in dCCA. Dedifferentiated SCs play a significant role in dCCA perineural invasion SCs have been firmly established as a novel cell type within the tumor microenvironment, playing a specific and cancer-promoting role in PNI ( 31 ). We focused our analysis on SCs within the tumor microenvironment, performing unsupervised clustering on 472 cells, identifying five distinct subclusters (Fig. 3 A). Utilizing a marker gene list curated from the Tabula Sapiens portal ( 32 ) and previous literature by Kastriti et al . ( 33 ), we observed that clusters 0 and 1 exhibited overexpression of myelinating SC (mSC) markers like EGR2 , MPZ , and PMP22 . Cluster 2 displayed upregulation of well-defined non-myelinating SC (nmSC) markers such as IGFBP5 , TAGLN2 , TPM1 , and A2M . Notably, cluster 4 preferentially expressed genes indicative of SC precursors (SCP): CD69 , BTG1 , CD52 , CYBA , and LTB (Fig. 3 A, Figure S3A). Among these clusters, MPZ + mSCs (cluster 0), PMP22 + mSCs (cluster 1), and SCPs (cluster 4) were predominantly located in cancer-adjacent tissues. Conversely, nmSCs (cluster 2) and cluster 3 had a greater proportion of cells distributed within cancer tissues (Fig. 3 B). Figure 3 C illustrates the unique transcriptomic signatures of all SC subsets identified in dCCA. To investigate the developmental pathways and potential roles of these distinct SC subclusters in dCCA with PNI, we first employed CytoTRACE to estimate the differentiation degree of each subcluster. As expected, the SCP cluster, representing multipotent embryonic progenitors for many neural cells ( 34 , 35 ), possessed the highest differentiation score (Figure S3B). Then, we reconstructed the SCs into a pseudotime trajectory using Monocle2, designating the SCP cluster as the starting point. Five distinct cell states (S1-S5) and a primary trajectory route were identified (Fig. 3 D). We observed that cluster 3 was positioned in close proximity to the differentiation starting site and characteristically expressed the dedifferentiation markers GFAP and NCAM1 (Fig. 3 D). Furthermore, we found that cluster 3 also upregulated the myelin gene SOX2 and the immature genes NGFR and L1CAM (Fig. 3 E), aligning well with the reprogramming process of dedifferentiated SCs described by Jessen et al . ( 36 ). Therefore, we classified cluster 3 SCs as dSC cells. Previous studies have indicated that both mSCs and nmSCs can contribute to cancer progression by transitioning to the dSC phenotype characteristic of repair SCs in cancer ( 31 , 36 ). Our analysis further revealed that PMP22 + mSCs and certain nmSCs (specifically cluster S4) initially transitioned to cluster 0 ( MPZ + mSCs) during the dedifferentiation process. Notably, this transition occurred with minimal upregulation of immature genes, while JUN expression increased but SOX2 remained relatively unchanged. Indeed, SOX2 is also a myeloid gene suppressor. In contrast, SOX2 expression became prominent during the dSC stage (Fig. 3 D-E). These findings suggest that the abandonment of myelin differentiation in dSCs may precede the activation of the immature phenotype, and different myeloid gene suppressors seem to act asynchronously. Overall, for PNI-positive dCCA, most SCs transition from SCPs to GFAP + dSCs, traversing an intermediate state (Cluster 0, MPZ + mSCs). Ultimately, they may develop into PMP22 + mSCs or nmSCs. Our analysis provides a comprehensive ecological map and trajectory evolution of SCs in PNI-positive dCCA. Gene ontology (GO) analysis revealed that MPZ + mSCs were significantly enriched in neuron apoptosis processes, neuron death, and tumor necrosis factor-mediated signaling pathways, possibly reflecting the damage response induced by cancer cell invasion (Figure S3C). Conversely, enriched GO terms for PMP22 + mSCs were associated with neural support and regeneration, including axonogenesis, axon development, and regulation of synapse maturation (Figure S3C). nmSCs were characterized by a high level of extracellular matrix similar to fibroblasts, while GO terms of GFAP + dSCs were enriched in cholesterol binding, lipid transfer activity, and phosphatidylcholine binding, indicating their high metabolic properties (Figure S3C). Finally, GO analysis of SCPs revealed their enrichment in pathways such as activation of the immune response, regulation of T cell activation, and neutrophil migration, suggesting a potential role in immune regulation (Fig. 4 F). To further elucidate the metabolic landscape of SCs in PNI-positive dCCA, scMetabolism was employed ( 37 ) to systematically quantify metabolic activities at single-cell resolution. We computed metabolic pathway activity scores for all 63 metabolic pathways annotated in scMetabolism and found that GFAP + dSCs exhibited the highest metabolic activity. Among these pathways, pyruvate metabolism, lactate metabolism, glycerolipid metabolism, and fatty acid biosynthesis were markedly activated in GFAP + dSCs (Fig. 3 F). To explore the influence of each SC cluster on dCCA prognosis, CIBERSORTx was applied to determine the percentage of diverse SC types across the TCGA-CHOL samples (Table S5). High infiltration of GFAP + dSCs was associated with an inferior prognosis (Fig. 3 G, Figure S3D). Similar results were obtained in the GSE107943 validation cohort (Figure S3E, Table S6). To investigate the contribution of GFAP + dSCs to the occurrence of PNI in dCCA, the TCGA-CHOL samples were sorted into PNI and non-PNI groups founded on the presence or absence of concomitant PNI. We observed that the PNI group displayed significantly higher infiltration of GFAP + dSCs (Fig. 3 H). Additionally, 22 dCCA patients from the First Affiliated Hospital of Zhengzhou University were enrolled in our internal cohort (Zhengzhou-dCCA cohort). Hematoxylin and eosin (H&E) staining confirmed that all pathological sources were tumor tissues (Figure S4A). Immunohistochemistry (IHC) analysis demonstrated that the positive rates of GFAP and NCAM1 proteins in the neural tissue of PNI samples were higher than those in non-PNI samples (Fig. 3 I, Table S7). Collectively, these data suggest that GFAP + dSCs possess high metabolic characteristics and play crucial roles in the PNI-positive microenvironment. Interactome landscape across NEAT1 + malignant cells and GFAP + dSCs in the PNI-related dCCA microenvironment To elucidate the crosstalk between NEAT1 + malignant cells and GFAP + dSCs within the tumor microenvironment during PNI progression, we investigated intercellular communication by simulating ligand-receptor interactions using CellChat. A total of 116 pairs of interactions were identified across the four cell types we classified. Notably, interactions between NEAT1 + malignant cells and GFAP + dSCs were more frequent (Fig. 4 A). A similar pattern was observed in terms of the strength of intercellular interactions (Figure S5A). These results underscore the critical roles of NEAT1 + malignant cells and GFAP + dSCs in PNI-positive dCCA. Subsequently, we utilized CellChat’s pattern recognition to identify major secretory signaling events of various cell types (Fig. 4 B). When NEAT1 + malignant cells served as the signal source and GFAP + dSCs as the signal input, the CDF15 - TGFBR2 interaction exhibited the highest interaction score (Fig. 4 B). Previous studies have demonstrated that inactivation of the TGFBR2 gene leads to uneven and severely underdeveloped dedifferentiated SC invasion in mice ( in vivo ), hindering their involvement in the bridge regeneration process after nerve injury ( 38 ). We also noted that the CDF15 gene was predominantly expressed in NEAT1 + malignant cells, while TGFBR2 was generally distributed across all SC types (Fig. 4 C). Conversely, when GFAP + dSCs sent ligands to NEAT1 + malignant cells, the primary interaction occurred through the BTC - EGFR pathway. The role of EGFR in cancer progression and as a therapeutic target in various human malignancies, including cholangiocarcinoma, lung cancer, colon cancer, and breast cancer, has been well-established ( 39 – 42 ). Analyzing the receptor-ligand expression distribution, we found that BTC was almost exclusively expressed in GFAP + dSCs, while EGFR was predominantly expressed by NEAT1 + malignant cells. Therefore, the BTC - EGFR interaction pair might represent a characteristic mode of communication between GFAP + dSCs and NEAT1 + malignant cells (Fig. 4 B-D). To investigate the significance of the NEAT1 + malignant cell- GFAP + dSC interaction within the tumor microenvironment (TME), we utilized SCENIC ( 43 ) to decipher the gene regulatory network (GRN) of these cell types. The GRN differed among the subtypes of both malignant cells and SCs (Figure S5B-C). We identified four key genes in the GRN of NEAT1 + malignant cells: SREBF2 , ATF3 , RFX2 , and JUN . These genes have previously been shown to be upregulated in damaged neurons and regulate oxidative stress during the dedifferentiation of neighboring SCs ( 44 , 45 ). JUN is known to control myelin cell dedifferentiation and the activation of repair programs ( 46 ). Conversely, multiple oncogenic transcription factors, including ETS1 , EP300 , SMAD4 , and ELK4 , were upregulated in GFAP + dSCs (Figure S5B-C). Jin et al . reported that tumor-derived extracellular vesicles promote renal cell carcinoma invasion and metastasis by transferring MALAT1 facilitating the binding of the transcription factor ETS1 and the TFCP2L1 promoter ( 47 ). Interestingly, MALAT1 is one of the genes that characterize NEAT1 + malignant cells. In conclusion, our data highlight the close communication between NEAT1 + malignant cells and GFAP + dSCs within the PNI-associated dCCA microenvironment and identify potential transcription factor candidates for further investigation. Hypoxia induces lactate secretion from cancer cells and further promotes SC dedifferentiation Previous research demonstrated that pancreatic cancer cell supernatants under hypoxic conditions can induce GFAP activation in human SCs ( 48 ). Notably, hypoxia is also a hallmark feature of NEAT1 + malignant cells. To investigate the mechanism of SC dedifferentiation induced by hypoxic cells, we initially cultured CCLP1 and HUCCT1 cell lines under hypoxic conditions in vitro to simulate the in vivo hypoxic TME. After a 48-h incubation under either normoxic or hypoxic conditions, HIF-α levels were detected via Western blot analysis to assess the successful induction of hypoxic stress in the cancer cells. The results indicated a significant enhancement of HIF-1α expression under hypoxic conditions (Fig. 5 A), confirming the effectiveness of our hypoxia modeling. Subsequently, we stimulated ipNF95.6 human SC cells with the modeled cancer cell supernatants to evaluate the activation of SCs by the hypoxic cholangiocarcinoma cell microenvironment. A significant increase in GFAP protein expression was observed when ipNF95.6 cells were exposed to the supernatant of the hypoxia group (Fig. 5 B). Given that cancer cells consume substantial amounts of oxygen and nutrients, secreting excess lactate ( 49 ), and the high lactate metabolic activity of dedifferentiated cells in our previous analysis, we sought to determine whether SC dedifferentiation was related to lactate within the tumor's hypoxic microenvironment. We initially measured lactate levels in the cell supernatant of hypoxic cancer cells. Our findings revealed a significant elevation of lactate levels within the supernatant of hypoxic cancer cells (Fig. 2 C). Similarly, we measured lactate levels in 22 CHOL tissues from the Zhengzhou-CHOL cohort, comparing the GFAP protein-negative and GFAP protein-positive nerve groups. We observed significantly higher lactate levels in the GFAP protein-positive nerve group (Fig. 5 D). For NCAM1 protein, there was a trend towards higher lactate content in the NCAM1 protein-positive nerve group, although these results were not statistically significant. Furthermore, we categorized all SCs from previous single-cell data into high and low lactate metabolism groups according to the median lactate metabolic activity score. We found that the expression of multiple dedifferentiation-related SC markers, including L1CAM , JUN , NCAM1 , GFAP , and NGFR , was increased in the high lactate metabolism group (Fig. 5 E). To examine the impact of lactate on SC dedifferentiation, we conducted a series of experiments. Referencing a previous study ( 50 ), we established a gradient lactate concentration (0, 10, 20, 40, 80, and 160 mM) to determine the optimal lactate concentration. SCs exposed to different lactate levels were cultured for 8 h, and their viability was assessed. The results indicated a dramatic decrease in SC viability at a lactate concentration of 20 mM. Consequently, we selected a lactate concentration of 10 mM for subsequent experiments (Fig. 5 F). Next, we analyzed multiple representative dedifferentiation-related genes, among which the mRNA and protein levels of NCMA1 , GFAP , and SOX2 were noticeably upregulated in lactate-induced SCs (Fig. 5 G-H). Additionally, we obtained cross-species validation in RSC96 (a rat Schwann cell line, Fig. 5 I), indicating that the evolutionary process of lactate-induced dedifferentiation in SCs might be conserved. Collectively, these findings indicate that lactate produced by hypoxic cancer cells promotes the dedifferentiation of SCs. Cancer cell-derived lactate upregulates HMGB1 in SCs, which further promotes the carcinogenic behavior of cholangiocarcinoma cells HMGB1 was initially reported to be released from lipopolysaccharide-stimulated macrophages and to function as a pro-inflammatory factor in sepsis ( 51 ). More recent studies have demonstrated that stromal cells, such as tumor-associated macrophages, upregulate intracellular HMGB1 expression upon lactate stimulation, thereby promoting cancer progression ( 52 , 53 ). Interestingly, our single-cell data revealed that HMGB1 was generally upregulated in GFAP + dSCs, and the percentage of GFAP + cells within SCnn cells correlated with the percentage of high HMGB1 -expressing cells (Fig. 6 A, Figure S6A). Furthermore, a protein-protein interaction (PPI) network was constructed utilizing the STRING database v12.0, linking HMGB1 with 14 SC dedifferentiation-related genes. The PPI network indicated an interaction between HMGB1 and GFAP and JUN (Figure S6B). To further investigate the relationship between SC dedifferentiation and HMGB1 expression, we performed IHC in the Zhengzhou-CHOL cohort (Figure S6C). The images revealed higher HMGB1 IHC scores in GFAP and NCAM1 proteins in the neural tissue of positive groups were higher than those of negative groups, although the latter showed no statistical significance (Fig. 6 B). Additionally, we found that HMGB1 protein was significantly upregulated after stimulation of ipNF95.6 cells with hypoxia-treated cancer cell supernatants (Fig. 6 C). To determine if HMGB1 expression in Schwann cells was similarly linked to lactate secreted in the tumor hypoxic microenvironment, immunofluorescence experiments demonstrated an increase in the cytoplasmic level of HMGB1 in lactate-treated ipNF95.6 cells (Fig. 6 D). To investigate the role of HMGB1 in SC dedifferentiation and its impact on tumor progression, we introduced glycyrrhizin (1 nM) to inhibit HMGB1 expression in subsequent protein immunoblotting and immunofluorescence experiments ( 54 ). Our findings revealed a significant elevation of HMGB1 protein levels in lactate-stimulated SCs, which was effectively inhibited by glycyrrhizin (Fig. 6 E, Figure S6D). Next, we sought to understand whether SCs stimulated with lactate promoted tumor progression through HMGB1 . Co-culture experiments with lactate-induced SCs demonstrated accelerated cell migration and invasion in both types of cancer cells. However, glycyrrhizin reversed the lactate-induced effect (Fig. 6 F-G). To further evaluate the functional role of lactate-induced dedifferentiated Schwann cells in CCA progression, we conducted in vivo experiments using xenograft mice. Mice injected with a mixture of lactate-stimulated SCs and CCLP1 cells exhibited larger tumor volumes. Notably, glycyrrhizin attenuated the tumor growth-promoting effect of lactate-stimulated SCs through HMGB1 inhibition (Fig. 6 H-I). Considering that the nuclear protein HMGB1 is released in response to diverse stimuli, including lactate ( 55 , 56 ), we focused on the expression level of HMGB1 within tumor cells after co-culture with Schwann cells. Lactate-treated SCs elevated the level of HMGB1 within cancer cells, while glycyrrhizin inhibited this elevation (Figure S6E). Collectively, these results suggest that SCs enhance the invasion and migration of cancer cells through lactate-induced upregulation of HMGB1 . Discussion At present, dCCA, a subtype of CCA, remains a highly lethal disease despite significant advancements in scientific understanding and clinical management ( 2 ). In recent years, numerous studies have been conducted on the molecular pathogenesis of CCA specially for iCCA, yet the unraveling of the molecular complexity of dCCA remains limited, and there are no approved targeted therapies with demonstrated clinical benefit. PNI, a common pathological feature in dCCA, is strongly associated with postoperative recurrence and poor prognosis ( 57 ). Several studies have highlighted the pivotal role of dSCs in PNI and cancer progression ( 10 , 58 , 59 ). A deep appreciation for the cellular ecosystem of PNI-associated dCCA and the potential molecular mechanisms underlying the contribution of dSCs to PNI remains an unmet clinical need. In our study, we utilized scRNA-seq to comprehensively map the transcriptomic landscape of human PNI-positive dCCA, unveiling novel cell-cell communications between dCCA cells and dSCs at single-cell resolution. Through scRNA-seq analysis, we identified multiple distinct cell types within PNI-positive dCCAs. Lymphoid cells predominated in PNI-positive dCCAs, accounting for over 30% of all cells, followed by myeloid cells and epithelial cells (both malignant and normal epithelium). The distribution of individual cell subsets within a single sample varied significantly, demonstrating substantial inter-tumor heterogeneity. scRNA-seq profiling enabled the definition of three distinct malignant subtypes. These three malignant subtypes exhibited specific differentially expressed genes (DEGs) and potential transcription factors (TFs). Importantly, we observed that an accumulation of NEAT1 + malignant cells was related to poorer clinical outcomes and the development of PNI in patients with dCCA, suggesting the tumor-promoting characteristics of NEAT1 + malignant cells. Notably, we found that NEAT1 + malignant cells displayed a highly hypoxic profile. Hypoxia-induced NEAT1 has been reported to be mediated by HIF-2α transcriptional activity ( 60 , 61 ). CNV-based analysis revealed a significantly higher proportion of CNV-high malignant cells within NEAT1 + malignant cells than other malignant cell subtypes, confirming the malignant nature of PNI-associated malignant cells. Our research employed pseudotemporal trajectory analysis to identify three distinct malignant cell states and further characterized their developmental dynamics. Among these states, NEAT1 + malignant cells might represent an earlier stage of differentiation in dCCA cells. We identified several NEAT1 + malignant cell-associated genes, many of which are related to hypoxic and oncogenic signaling pathways, such as VEGFA , AKT , JUN , and KRAS . Previous studies have reported that JUN promotes de-differentiation of SCs after neural injury by inhibiting P0 , myelin basic protein genes , and KROX20 ( 36 , 62 ). Interestingly, JUN was also a predicted regulator of NEAT1 + malignant cells. This suggests the possibility of JUN -mediated cellular communication between these malignant cells and dSCs, which warrants further investigation. Additionally, upregulated MALAT1 in NEAT1 + malignant cells was associated with synapse formation and neuronal cell survival ( 63 ), potentially resulting from the close interaction between NEAT1 + malignant cells and dSCs predicted at the single-cell level. In conclusion, our findings highlight that NEAT1 + malignant cells, characterized by hypoxia, are a representative group of malignant cells in PNI-positive dCCA. As major constituent cells of nerves, SCs have been demonstrated to promote tumor growth and play a pivotal role in PNI across multiple tumor types. In this study, we provided hitherto undocumented evidence of five distinct SC subtypes in human dCCA and its adjacent tissues. Notably, nmSCs and GFAP + dSCs exhibited a higher proportion of cells within dCCA tissues, while SCPs, PMP22 + mSCs, and MPZ + mSCs were predominantly found in the adjacent tissues, highlighting the heterogeneity of the neurological tissue microenvironment in dCCA. Our analysis revealed that GFAP + dSCs expressed high levels of myeloid and immature SC genes, suggesting they might be a biochemically and metabolically active subpopulation of SCs. Subsequently, we verified that cholangiocarcinoma cell-derived lactate is a metabolite that induces and maintains GFAP + dSC dedifferentiation. It has been reported that monocarboxylate transporter protein ( MCT ) is highly expressed in perineuronal cells and facilitates lactate uptake as its preferred energy metabolite ( 64 , 65 ). The dependence of peripheral nerve function on lactate metabolism was further emphasized in a study by Morrison et al ., where MCT1 deficiency impeded nerve regeneration after peripheral nerve injury in mice ( 66 ). Importantly, pseudotemporal trajectory analysis and survival analysis indicated that GFAP + dSCs, following SCPs, are the most naïve and represent a malignant SC population within the PNI-positive dCCA neural microenvironment, potentially originating from MPZ + mSCs. Therefore, the GFAP + dSCs subpopulation may serve as a promising therapeutic target for dCCA patients with concomitant PNI. HMGB1, a representative injury-associated molecule, has been implicated in various pathological processes, including neurodegenerative diseases, autoimmunity, and cancer progression ( 67 , 68 ). Peripheral nerve injury can induce HMGB1 expression through the proliferation of SCs and infiltration of macrophages within nerves ( 69 , 70 ). HMGB1 expression is significantly elevated in perihilar cholangiocarcinoma tissues ( 71 ) and is associated with poor prognosis, lymphatic invasion, and direct involvement in cholangiocarcinoma proliferation and angiogenesis ( 71 , 72 ). Recent studies have demonstrated that lactate stimulates macrophage M2 polarization and secretes HMGB1 , thereby promoting glioma cell invasion ( 73 ). IHC staining also confirmed that GFAP and NCAM + peripheral nerves express high levels of HMGB1 internally, which prompted us to investigate whether SCs also act as a lactate-induced " HMGB1 reservoir," contributing to neural infiltration by dCCA cells. In this study, we found that cholangiocarcinoma cell-derived lactate stimulated the dedifferentiation of SCs and significantly induced HMGB1 expression in GFAP + dSCs, enhancing malignancy. It is widely believed that HMGB1 may directly contribute to tumor cell metastasis by modifying extracellular matrix components and regulating cell adhesion properties ( 74 ) or enhance tumor cell progression by inducing melanoma inhibitory activity ( MIA ) ( 75 ). Cellular immunofluorescence confirmed a significant upregulation of HMGB1 in cholangiocarcinoma cells following co-culture with SCs that exocrine HMGB1 , suggesting its potential role in exacerbating their malignant behavior. To sum up, our study presents a uniquely matched set of transcriptomic landscapes within the tumor microenvironment of paraneoplastic and PNI-positive dCCA tumor samples, offering a valuable resource for elucidating SC diversity in PNI-positive dCCA. This study also highlights the intra-tumor crosstalk between PNI-associated malignant cells and dedifferentiated SCs. Future research is warranted to corroborate the molecular mechanisms underlying dCCA PNI, and our dataset can serve as a valuable tool for designing targeted therapeutics against PNI-positive tumors. Methods Patients and clinical sample collection Two patients with dCCA who did not receive preoperative chemotherapy or radiotherapy participated in this study. Informed consent was obtained from all participants, who were requested to donate their tumor tissues and corresponding peri-tumor tissues for scientific research. The study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (ethical approval number: 2018-KY-83). Tissue samples were transported on ice and processed within 30 min of acquisition. scRNA-seq and data analysis of cholangiocarcinoma tissue Single-cell suspensions were prepared for each sample. Cell viability was ensured to be above 70%, and the cell concentration was adjusted to 300–600 cells/µL. scRNA-seq was performed using the 10X Genomics Chromium Single Cell 3' platform following the manufacturer's instructions. The generated count matrices were converted to a Seurat object using the Seurat package (version 4.4.0) ( 76 ). Cells expressing fewer than 200 genes or with mitochondrial reads exceeding 40% were excluded from downstream analysis. Batch effect correction was conducted using the Harmony package ( 77 ), and the filtered gene-barcode matrices were normalized using the LogNormalize method. The top 3 000 highly variable genes for principal component analysis (PCA) were identified using the 'FindVariableFeatures' function. The top 30 principal components (PCs) were then selected for Uniform Manifold Approximation and Projection (UMAP) visualization of the cells. For cell clustering, the 'FindClusters' function was employed at a resolution of 0.3. Subgroup cell clusters were analyzed by selecting the top 30 PCs and clustering at various resolutions, which were determined through visual inspection. Distinguish malignant and non-malignant epithelial cells based on inferred CNVs Initial CNVs were estimated using the inferCNV package (version 1.12.0), as previously described ( 78 ). To minimize the impact of genes with extreme expression, the expression values were re-standardized and restricted to the range [-2,2]. For each cell, the mean of the squared CNVi (CNV of the ith window) across the genome was calculated as the CNV signal. Additionally, the CNV correlation values were calculated by correlating the CNV profile of a single cell with the average CNVi profile of the top 5% of single cells with the highest CNV scores. Epithelial cells with a CNV signal above 0.225 and a CNV correlation above 0.45 were classified as malignant. Identification of high-confidence hypoxic cells using the CHPF model CHPF ( 25 ) is an open-source modeling framework designed to predict cellular hypoxia status. The CHPF script was executed in Python (version 3.11.5). The single-cell expression profile and seven pre-selected hypoxia-related gene sets served as input files for the construction of the prediction model. The final formula is provided below: P ( x ) = \(\:\frac{{\sum\:}_{i=1}^{n}{W}_{i}\left(x\right){f}_{i}\left(x\right)}{{\sum\:}_{i=1}^{n}{W}_{i}\left(x\right)}\) ( n = 100) Cells with P ( x ) > 0.5 were considered as hypoxic cells. Pseudotime analysis by Monocle Pseudo-time analysis and transcriptome dynamic analysis along the pseudo-time trajectory were conducted using Monocle2 (version 2.26.0) ( 79 ) with the default parameters recommended by the developer. Deconvolution analysis We employed CIBERSORTx, a deconvolution analysis tool, to investigate gene expression within the tumor microenvironment. Our analysis focused on the 10X single-cell RNA sequencing data, specifically looking for DEGs between epithelial cell subtypes and SC subtypes. These genes were used to create a signature matrix. To deconvolute the bulk RNA-sequencing data, we employed two separate reference sources: 1) data from the TCGA-CHOL cohort within The Cancer Genome Atlas (TCGA); and 2) an RNA-sequencing dataset (GSE107943) downloaded from the Gene Expression Omnibus (GEO) database. These datasets served as the mixture files for CIBERSORTx analysis. H&E staining and IHC assay 22 cases of dCCA tissues that underwent pancreaticoduodenectomy were obtained from the First Affiliated Hospital of Zhengzhou University between 2021 and 2024. Tissues were fixed with 4% paraformaldehyde, embedded in paraffin, and cut into 4 µm sections. For H&E staining, the sections were stained with hematoxylin (BA4097, BaSo Diagnostics Inc., Zhuhai, China) for 5 min and eosin (BA4098, BaSo Diagnostics Inc.) for 3 min. For IHC assay, 4 µm thick sections were deparaffinized, rehydrated, and blocked. Primary antibodies were incubated at 4°C overnight, followed by incubation with goat anti-mouse IgG H&L HRP (1:4 000, ab205718, Abcam, Boston, USA) at room temperature for 2 h. The primary antibodies used in this study included GFAP (1:5 000, SA00001-1, Proteintech, Wuhan, China), NCAM1 (1:5 000, 14255-1-AP, Proteintech), and HMGB1 (1:400, 66525-1-Ig, Proteintech). The staining extent score (75%, score = 4) and staining intensity (negative, score = 0; weak, score = 1; moderate, score = 2; strong, score = 3) were assessed using ImageJ software 1.46r. IHC results was scored by multiplying the staining extent score by the intensity score. All H&E stained and IHC sections were scanned with a Pannoramic MIDI II scanner (3D HISTECH Ltd., Hungary). Cell culture and treatment CCLP1 (JNO-H0653) and HUCCT1 (BNCC337995) human cholangiocarcinoma cell lines were obtained from Jennio Biotech Co., Ltd. (Guangzhou, China) and Beina Chuanglian Biotechnology Institute (Beijing, China), respectively. Human ipNF95.6 SCs (CTCC-001-0379, Meisen CTCC, Panan, China) and rat RSC96 SCs (CL-0199, Pricella Biotechnology Co., Ltd., Wuhan, China) were maintained in our laboratory. All cells were cultured in DMEM (12100, Solarbio, Beijing, China) medium supplemented with 10% fetal bovine serum (C04001-500, VivaCell, Shanghai, China). The normoxia group cells were cultivated at 37°C in a 5% CO 2 humidified incubator (Galaxy 170R, Eppendorf, Hamburg, Germany). For hypoxia induction, cells were transferred to a tri-gas incubator (Galaxy 48R, Eppendorf) and incubated under 1% O 2 , 5% CO 2 , and 94% N 2 for 48 h prior to commencing subsequent experiments. A non-contact coculture system was established using 24-well plates containing 0.4µm polyethylene terephthalate membrane filters (Corning, NY, USA) to separate the lower and upper chambers. ipNF95.6 cells, subjected to various treatments, were seeded in the upper chamber at a density of 1×10 5 cells/mL. CCLP1 or HUCCT1 cells were then inoculated in the lower chamber at a density of 1.5×10 5 cells/mL. Following a 48-h incubation period, cholangiocarcinoma cells were harvested for subsequent experiments. To stimulate lactate production, SCs were cultured for 8 h with increasing concentrations of lactate (0, 10, 20, 40, 80, and 160 mM; L1750, Merck, NJ, USA). Glycyrrhizin (B20417-20mg, Yuanye Bio-Technology, Shanghai, China), a direct inhibitor of HMGB1, was added to SCs ( 54 ) at a concentration of 1 nM in conjunction with lactate to suppress HMGB1 expression in these cells. The supernatant of cancer cells cultured under normoxic or hypoxic conditions was collected by centrifuging at 500×g for 10 min, followed by a second centrifugation at 2 000×g for 20 min. After any necessary pre-treatments, SC supernatant was collected using the same procedure. The collected supernatants were stored at -80°C and used within one month. Cell–cell interaction analysis Intercellular communication between malignant and SC types was investigated using the CellChat package (version 1.6.1) ( 80 ). The Seurat-normalized data was transformed into a CellChat object using the 'createCellChat' function. Subsequently, the 'computeCommunProbPathway' function was employed to infer intercellular communication at the signaling pathway level. SCENIC analysis SCENIC (version 1.3.1) was utilized to evaluate the transcriptional activity of malignant and SCs ( 43 ). SCENIC was implemented in R using the motif databases of RcisTarget and GRNboost (corresponding to GENIE3 1.20.0, AUCell 1.22.0, and RcisTarget 1.18.2). Raw UMI counts served as input for the analysis. Western blot assay The total proteins were extracted with RIPA buffer (R0010, Solarbio). Proteins were separated by 10% SDS-PAGE and transferred to nitrocellulose membranes. The membranes were incubated with primary antibodies at 4°C overnight, and specific binding of the primary antibodies was detected with peroxidase-labeled goat anti-mouse (1:4 000, SA00001-1) or goat anti-rabbit (1:4 000, SA00001-2) secondary antibodies (Proteintech). The following primary antibodies were used: HIF1A (1:4 000, ab51608, Abcam), GFAP (1:5 000, 60190-1-Ig, Proteintech), NCAM1 (1:5 000, 60238-1-Ig, Proteintech), SOX2 (1:1 000, #2748, CST, Boston, USA) and HMGB1 (1:3 000, 66525-1-Ig, Proteintech) and β-actin (1:40 000, 66009-1-Ig, Proteintech). Lactate measurement The lactate concentrations in cells or tissues were determined by an L-Lactate Assay Kit (A019-2-1, Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Samples were prepared according to the manufacturer’s instruction and the lactate levels of the samples were calculated by measuring the absorbance at 530 nm. Cell cytotoxicity experiment For the thiazolyl blue tetrazolium bromide (MTT) assay, MTT (M5655, Sigma, Shanghai, China) was dissolved in DMEM at a concentration of 5 mg/mL. Cells were incubated with MTT for 4 h at 37°C under 5% CO 2 . After removing the MTT, the formed MTT-formazan crystal was dissolved in DMSO (150 µL/well). Absorbance at 490 nm was measured using a microplate reader (Spark, Tecan, Switzerland). The results were expressed as the percentage change in absorbance compared to untreated control cells, which were set to 100%. Data represent the average of triplicate measurements from three independent experiments. Real‑time quantitative polymerase chain reaction (RT‑qPCR) Total RNA from cells was extracted using TriQuick Reagent (Solarbio). cDNA was synthesized using NovoScriptPlus All-in-one 1st Strand cDNA Synthesis SuperMix (E047-01B, Novoprotein Scientific Inc., Shanghai, China). RT-qPCR was conducted in a 20 µL reaction volume containing forward and reverse primers, cDNA, and NovoStart SYBR qPCR SuperMix Plus (E096-01A, Novoprotein Scientific Inc.). All primers were synthesized by Sangon Biotech and normalized to GAPDH. RNA folding changes were quantified using the 2 −ΔΔCt method. The primer sequences are listed in Table S8. Immunofluorescence (IF) staining Cell climbing slices were sterilized with 75% alcohol, air-dried in 24-well plates, and inoculated with malignant or SCs. After that, cells were fixed with 4% paraformaldehyde, permeabilized with 0.2% Triton X-100 for 15 min at 4°C, and blocked with 5% bovine serum albumin. Primary HMGB1 antibody (1:200, 66525-1-Ig, Proteintech) was added overnight at 4°C. Cells were then incubated with goat anti-mouse IgG (H&L) secondary antibody (1:400, Alexa Fluor 488, GB25301, Servicebio, Wuhan, China) or (1:200, Alexa Fluor 594, AB0152, Abways, Shanghai, China) for 2 h at room temperature, protected from light, and counterstained with DAPI for 10 min. Images were captured using a DM4B microscope system (Leica, Wetzlar, Germany). Wound healing assay A coculture system was employed for the scratching experiment. Following coculture completion, the upper chamber was removed, and the cells in the lower chamber were rinsed with phosphate-buffered saline (PBS). Cells were maintained in culture until reaching 90% confluence. Two lines were then scratched using a 200 µL pipette tip. Nonadherent cells were washed away twice with PBS and incubated in serum-free DMEM for 24 h. Microscopic observation and photography were performed at 0 and 24 h. The ImageJ software (Version 1.6.0) was utilized to analyze the wound healing rate by quantifying the wound closure area. Cell invasion assay A cell invasion assay was conducted using 24-well plates equipped with 8-µm pore polycarbonate membrane inserts (Corning, NY, USA). A 200 µL cell suspension (serum-free DMEM) was seeded in the upper chamber, and Medium (600 µL) containing 10% FBS was added to the lower chamber. Following incubation at 37°C for 48 h, non-migratory cells on the top surface of the inserts were gently removed with cotton swabs. Subsequently, the inserts were fixed in 4% paraformaldehyde for 15 min, and the cells were stained with 0.2% crystal violet. The number of cells that invaded the membrane was quantified under a light microscope. Tumorigenicity assay Tumor xenografts were established in 5-week-old BALB/c nude mice obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd (Beijing, China). The mice were maintained in specific pathogen-free (SPF) condition with a 12-h light/dark cycle. After acclimatization for one week, 5×10 6 CCLP1 cells mixed with 2×10 6 ipNF95.6 cells were injected subcutaneously on the right flank of the mice. ipNF95.6 cells were pretreated with PBS, lactate, or lactate combined with glycyrrhizin for over three generations. Following inoculation, tumor volume was measured every three days and calculated using the formula V = ab 2 /2, where a is the long diameter and b is the short diameter. All animal experiments were approved by the Laboratory Animal Management Committee of Zhengzhou University. Statistical analysis The Shapiro-Wilk test was used to assess the normality of each distribution. Student's t -test or the Wilcoxon rank-sum test was employed to compare two groups, while ANOVA followed by Tukey's multiple comparisons test was used for multiple group comparisons. Data were analyzed using GraphPad Prism (version 8.0.1.244, GraphPad Software Inc., San Diego, USA). Kaplan-Meier curves were constructed and analyzed using the log-rank test. All data were presented as means ± standard deviation. * p < 0.05, ** p < 0.01, *** p < 0.001. Declarations Contributions Z.Z., X.S., and W.Z. conceived the project and designed the experiments. Z.Z. and C.Z. performed this experimental work and draft the manuscript. Z.Z. led the data analysis in collaboration with J.S. and X.S. X.S., Z.Z., and K.H. performed the HE and IHC experiments, and assessed all the histology in this study. H.T. and E.L. contributed with critical reagents and materials. K.C., C.J., and R.F. provided useful suggestions for this manuscript. Competing interests The authors declare no competing interests. Acknowledgments We greatly appreciate the donations of samples from enrolled patients and help with sample collection by clinical staff at the Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital of Zhengzhou University. We thank the Laboratory Animal Center of Zhengzhou University (Henan Institute of Medical and Pharmaceutical Sciences, Academy of Medical Science, Zhengzhou University) for their assistance with the experiment. This work was supported by grants from the Key Research and Development Program of Science and Technology Department of Henan Province [No. 241111312300 to W.Z.]. We also express gratitude to the Home for Researchers editorial team ( www.home-for-researchers.com ) for language editing service. Data Availability Statement RNA sequencing data and analysis code are available from W.Z. or Z.Z. on reasonable request. CHPF is available online at https://github.com/yihan1221/CHPF . 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Upregulation of HMGB1 in tumor-associated macrophages induced by tumor cell-derived lactate further promotes colorectal cancer progression. J Transl Med. 2023;21(1):53. Sims GP, Rowe DC, Rietdijk ST, Herbst R, Coyle AJ. HMGB1 and RAGE in inflammation and cancer. Annu Rev Immunol. 2010;28:367–88. Sasahira T, Kirita T, Oue N, Bhawal UK, Yamamoto K, Fujii K, et al. High mobility group box-1-inducible melanoma inhibitory activity is associated with nodal metastasis and lymphangiogenesis in oral squamous cell carcinoma. Cancer Sci. 2008;99(9):1806–12. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411–20. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289–96. 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Additional Declarations (Not answered) Supplementary Files SupplementalTables.xlsx Supplementaryfigures.docx Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2025 Read the published version in Cell Death & Disease → Version 1 posted Editorial decision: revise 06 Dec, 2024 Review # 2 received at journal 30 Nov, 2024 Review # 1 received at journal 18 Nov, 2024 Reviewer # 2 agreed at journal 13 Nov, 2024 Reviewer # 1 agreed at journal 12 Nov, 2024 Reviewers invited by journal 09 Nov, 2024 Submission checks completed at journal 11 Oct, 2024 Editor assigned by journal 10 Oct, 2024 First submitted to journal 10 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5240585","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375998774,"identity":"cd526a1b-5ed1-412f-b526-d88fb7b19027","order_by":0,"name":"Wenlong Zhai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIie3QsQrCMBCA4SuBdDl1TZG+QyGgDqKv0lLoJjg6iBYKHTsr+BB9hEjAvoZQqIuDUHBUU3ASie3mkH+6wH0EDsBk+sPsGIAAMOg3k8qKfxEUb0KbqQsBoMxvS2xZ1sv1JEidS1kjTN1ckOqsJRh5fHdiQTr0fY4Q8VzQsacjc4YQImULRUSIIINcqKf2F0UkPhRxjrFEeLYiVtJLFWHEShBEC4IRJ72MbVOMiHXwQr6XdKQnzcXwvuEDuyhv19XMzYqk0pKPmlORDvsmk8lk+t4Lmyg9s2WTYgoAAAAASUVORK5CYII=","orcid":"","institution":"the First Affiliated Hospital of Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Wenlong","middleName":"","lastName":"Zhai","suffix":""},{"id":375998775,"identity":"b9de4730-56d5-49db-bdc4-66da01cd6a1c","order_by":1,"name":"Ziyang Zu","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ziyang","middleName":"","lastName":"Zu","suffix":""},{"id":375998776,"identity":"83f8f964-16ef-4809-a528-6f04c148aa23","order_by":2,"name":"Chong Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Chong","middleName":"","lastName":"Zhang","suffix":""},{"id":375998777,"identity":"db0071b1-59fb-4007-a48f-6facf763d89f","order_by":3,"name":"Jianxiang Shi","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jianxiang","middleName":"","lastName":"Shi","suffix":""},{"id":375998778,"identity":"45bb00f0-d89a-4bf5-b808-f3f3d8002d31","order_by":4,"name":"Kunlun Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Kunlun","middleName":"","lastName":"Chen","suffix":""},{"id":375998779,"identity":"3d2f4d76-464a-47c5-8f4d-a97446bee323","order_by":5,"name":"Hongwei Tang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Tang","suffix":""},{"id":375998780,"identity":"a5909989-b29c-4183-9efb-30e9ae31ca90","order_by":6,"name":"Kaizhao Hu","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Kaizhao","middleName":"","lastName":"Hu","suffix":""},{"id":375998781,"identity":"aa745e1a-4646-4315-ba97-f14703b66789","order_by":7,"name":"Enchi Liu","email":"","orcid":"","institution":"the First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Enchi","middleName":"","lastName":"Liu","suffix":""},{"id":375998782,"identity":"e42d5b07-3ad2-4a54-b3da-8b0d0e90b24a","order_by":8,"name":"Chengyang Ji","email":"","orcid":"","institution":"the First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Chengyang","middleName":"","lastName":"Ji","suffix":""},{"id":375998783,"identity":"33fa15e1-08c7-4146-8d0d-f9ec78ea4a16","order_by":9,"name":"Ruo Feng","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ruo","middleName":"","lastName":"Feng","suffix":""},{"id":375998784,"identity":"07e52452-c918-4199-88e8-e20d4fad178c","order_by":10,"name":"Xiaojing Shi","email":"","orcid":"https://orcid.org/0000-0002-5792-1287","institution":"Academy of Medical Sciences, Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2024-10-10 14:48:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5240585/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5240585/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41419-025-07543-x","type":"published","date":"2025-03-27T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71056284,"identity":"eccc52ed-5816-44c6-82f7-3c1496fad225","added_by":"auto","created_at":"2024-12-10 16:15:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":416895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScRNA-seq profiling of 2 dCCAs.\u003c/strong\u003e (A) Schematic representation of the experimental strategy. Part of the pictures were adapted from Servier Medical Art (http://smart.servier.com). (B) Heatmap showing the expression of marker genes in the indicated cell types. (C) Chromosomal landscape of inferred large-scale CNVs in normal epithelial cells (top) and potentially malignant cells from 2 dCCA samples. Rows represent individual cells and columns represent chromosomal positions. Amplifications (red) or deletions (blue) were inferred by averaging expression over 100-gene stretches on the respective chromosomes. (D) UMAP plot of malignant and nonmalignant cells from 2 dCCA samples. (E) Boxplot showing the fraction of nonmalignant cells in peri-tumor and tumor. dCCA,distal cholangiocarcinoma;CNVs, copy number variations; UMAP, uniform manifold approximation and projection; SC, Schwann cell; SMC, smooth muscle cell.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5240585/v1/6cc64e60c674605dd92026ce.png"},{"id":71056289,"identity":"f5bd2cad-71d0-4489-94aa-c14969008cf2","added_by":"auto","created_at":"2024-12-10 16:15:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":656190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptional signatures and hypoxia heterogeneity of malignant cells.\u003c/strong\u003e (A) UMAP plot of three malignant subtypes. Pie charts for each subtype showing the contributing percentage of cells from each patient. (B) Heat map showing the top DEGs in each malignant subtype. (C) UMAP plot of malignant cells colored by hypoxia status. (D) Semisupervised pseudotime trajectory of malignant subtypes inferred by Monocle2. Trajectory is colored by pseudotime (top left), cell subtypes (top middle), hypoxia status (top right), cell states (left), CNV levels (middle), the expression dynamics of a selected marker gene PEAK1 (right), stemness signature scores (bottom left), metastasis signature scores (bottom middle), signature scores calculated based on the HO_LIVER_CANCER_VASCULAR_INVASION geneset (bottom right). (E) Malignant cells were grouped into different categories based on the CNV score. Ridgeline Plots show the distribution of CNV scores across different samples. The red dashed line indicates the threshold values. (F) CNV inferred by scRNA-seq data in patient P1. (G) The percentage of cells with high levels of hypoxia is positively correlated with the proportion of cells with high levels of CNVs. (H) Heatmap showing the scaled expression of DEGs across pseudotime trajectory in (D). Bar plots at the top of the heatmap are scale diagrams of different cell states, hypoxia status, CNV levels and cell subtypes during pseudotime differentiation trajectory. (I) Association of relative cell abundance (estimated by CIBERSORTx) and patient survival using the TCGA-CHOL cohort (n = 36) by COX regression analysis. (J) Kaplan–Meier curves of TCGA-CHOL patients (n = 36) showing the survival rates grouped by the cell abundance in malignant cell cluster 1. The p-value is calculated with two-sided log-rank test. (K) Violin plots displaying the cell abundance in malignant cell cluster 1 in non-PNI and PNI groups. non-PNI, n = 26 samples; PNI, n = 7 samples. The central mark indicates the median, and the bottom and top edges of the box indicate the first and third quartiles, respectively. The top and bottom whiskers extend the boxes to a maximum of 1.5 times the interquartile range. UMAP, uniform manifold approximation and projection; DEGs, differentially expressed genes; CNV, copy number variation; PNI,perineural invasion.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5240585/v1/d0b9d55a1ea01108e05f3773.png"},{"id":71056286,"identity":"33356288-680c-493a-ae3a-4fb067e42b20","added_by":"auto","created_at":"2024-12-10 16:15:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":854832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscription profiling of SCs in the tumour microenvironment of PNI-positive dCCA tissues. \u003c/strong\u003e(A) UMAP showing the five subtypes of SCs, colored by subclusters. (B) Distribution of SCs in different sample groups on the UMAP. Pie chart showing the proportion of two sample groups in each SC subcluster. (C) Violin plots (left) displaying the representative expression pattern across different subtypes of SCs. Dot plot (right) showing the expression of the top six subtype-specific gene markers in each subtype. (D) Semisupervised pseudotime trajectory of SC subtypes by Monocle2. Trajectory is colored by pseudotime (top left), cell states (top middle), cell clusters (top right), sample groups (bottom left), and expression dynamics of two marker genes GFAP (bottom middle) and NCAM1 (bottom right). (E) Dot plot illustrating the expression patterns of selected dSC gene markers in each SC subtype. (F) Dot plot showing the metabolic activity analysis of all SC subclusters by scMetabolism. The circle size and color darkness both represent the scaled metabolic score. (G) Association of relative cell abundance (estimated by CIBERSORTx) and patient survival using the TCGA-CHOL cohort (n = 36) by COX regression analysis. (H) Violin plots displaying the cell abundance in GFAP\u003csup\u003e+\u003c/sup\u003e dSC in non-PNI and PNI groups. non-PNI, n = 26 samples; PNI, n = 7 samples. The central mark indicates the median, and the bottom and top edges of the box indicate the first and third quartiles, respectively. The top and bottom whiskers extend the boxes to a maximum of 1.5 times the interquartile range. (I) Representative images (top left) of immunohistochemical expression of GFAP and NCAM1 in patients from the Zhengzhou-dCCA cohort (n = 22). Representative images (bottom left) of H\u0026amp;E staining assays of PNI and non-PNI patients from the Zhengzhou-dCCA cohort. The experiment was repeated once with similar results. Nerves are highlighted with dotted lines and tumor cells with arrows. Scale bars, 100 μm. Graphical representation (right) of the proportion of PNI and non-PNI patients from the Zhengzhou-dCCA cohort. Bar plot (right) showing the proportion of immunohistochemistry staining positively for GFAP and NCAM1 from PNI and non-PNI patients from the Zhengzhou-dCCA cohort. UMAP, uniform manifold approximation and projection; SCs, Schwann cells; dSC, dedifferentiated Schwann cell; PNI, perineural invasion.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5240585/v1/7edba0792af216783814d57a.png"},{"id":71057748,"identity":"44dfeaa2-1092-430b-b7cd-40f8b4faef33","added_by":"auto","created_at":"2024-12-10 16:31:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":496690,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell-cell communication between malignant cells and SCs. \u003c/strong\u003e(A) Cell-cell interaction network (top) of NEAT1\u003csup\u003e+\u003c/sup\u003e malignant cells, other malignant cells, GFAP\u003csup\u003e+\u003c/sup\u003e dSCs, and other SCs. The node size represents the number of interactions. The width of the edge represents the number of significant ligand-receptor interactions in two cell types. Bar plot (bottom) presenting the numbers of putative ligand-receptor pairs between malignant cells and SCs. (B) Bubble heatmap showing cells interaction strength for different ligand-receptor pairs. Dot size indicates p-value generated by the permutation test and dot color represents communication probabilities. Empty space indicates that the communication probability is zero. (C-D) UMAP plot showing expression levels of GDF15-TGFBR2 (C) and BTC-EGFR (D) ligand-receptor pairs in specific cell types. SCs, Schwann cells; dSCs, dedifferentiated Schwann cells; UMAP, uniform manifold approximation and projection.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5240585/v1/72d85d37d619cbf072a7b4eb.png"},{"id":71056288,"identity":"247a759e-80c4-40ea-beeb-3a1eb942eb7a","added_by":"auto","created_at":"2024-12-10 16:15:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":364979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypoxia condition induced lactate secretion of cancer cells, further facilitated SC dedifferentiation. \u003c/strong\u003e(A) The protein levels of HIF-α in CCLP1 and HUCCT1 cells under normoxia and hypoxia conditions. (B) The protein levels of GFAP in ipNF95.6 cells receiving supernatants from cancer cells under normoxia and hypoxia conditions. (C) Detection of lactate in the supernatant of CCLP1 and HUCCT1 cells by lactate assay kit. (D) Detection of lactate in positive and negative tissues for SC dedifferentiation markers (GFAP and NCAM1) in the Zhengzhou-dCCA cohort by lactate assay kit. (E) Expression levels of 10 SC dedifferentiation markers in SCs with high and low lactate metabolism. (F) The cytotoxic activity of lactate was measured using the MTT cell viability assay in ipNF95.6 cells. (G) Detection of mRNA expression \u0026nbsp;levels of seven SC dedifferentiation markers in ipNF95.6 cells by RT‑qPCR. (H) Western blotting assays detecting protein levels of SC dedifferentiation markers in ipNF95.6 and RSC96 cells. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. SC, Schwann cell; RT‑qPCR, Real‑time quantitative polymerase chain reaction.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5240585/v1/b53805573ad53fa67a8ffcec.png"},{"id":71057132,"identity":"13db5310-0130-4ac1-aed8-02bc18916f93","added_by":"auto","created_at":"2024-12-10 16:23:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":510111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCancer cell-derived lactate upregulated HMGB1 in GFAP+ dSCs and HMGB1 further promoted carcinogenic behaviors. \u003c/strong\u003e(A) The percentage of GFAP positive cells is positively correlated with the proportion of cells with high levels of HMGB1. (B) The correlation between IHC expression of SC dedifferentiation markers (GFAP and NCAM1) and IHC scores of HMGB1 in the Zhengzhou-dCCA cohort. (C) The protein level of HMGB1 in ipNF95.6 cells receiving supernatants from cancer cells under normoxia and hypoxia conditions. (D) The observation of HMGB1 protein levels in ipNF95.6 cells using immunofluorescence. (E) The protein levels of HMGB1in CCLP1 and HUCCT1 cells. (F) Wound healing assays and Trans-well cell migration (G) were performed to assess cell mobility. (H) Xenograft tumor model was established with a mixture of CCLP1 cells and ipNF95.6 cells. The arrows indicated the subcutaneous tumor. (I) Tumor volumes were documented every 3 days. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. SC, Schwann cell; IHC, immunohistochemistry.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5240585/v1/5e85cecf5c62ba89af4daca8.png"},{"id":79414676,"identity":"339092ff-3439-4453-b5e2-71d495da0682","added_by":"auto","created_at":"2025-03-28 07:07:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4831038,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5240585/v1/59fa38d6-e06e-47b7-8662-11a278bc5b9d.pdf"},{"id":71056283,"identity":"ef9e2b79-cd02-4139-aa94-96d6846c17c2","added_by":"auto","created_at":"2024-12-10 16:15:54","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5240585/v1/4103ce6407f59d3e8d1b6133.xlsx"},{"id":71056290,"identity":"b246bea0-ad85-4afe-84bd-eaa735758778","added_by":"auto","created_at":"2024-12-10 16:15:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4134925,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5240585/v1/0eb0c64595e721c781665ccc.docx"}],"financialInterests":"(Not answered)","formattedTitle":"Single-cell analysis reveals that GFAP+ dedifferentiated Schwann cells promote tumor progress in PNI-positive distal cholangiocarcinoma via lactate/HMGB1 axis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCholangiocarcinoma (CCA) represents the most prevalent malignancy of the biliary system (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Depending on its anatomical location, CCA is typically categorized into intrahepatic cholangiocarcinoma, periportal cholangiocarcinoma, and distal cholangiocarcinoma (dCCA) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), with the latter localized to the common bile duct below the cystic duct insertion (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Over nearly three decades, the diagnostic morbidity and mortality of dCCA have continued to increase (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Due to early asymptomatic or nonspecific symptoms, many cases of dCCA are diagnosed in advanced stages, leading to limited available therapeutic options and an extremely poor prognosis. Surgical resection followed by adjuvant therapy may improve survival outcomes in patients with dCCA, but the high recurrence rate after dCCA surgery remains a challenge in clinical management (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Perineural invasion (PNI), refers to the characteristic biological process by which cancer cells invade nerves and spread along the perineurium (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In a recent cohort study, PNI was a common pathological phenomenon, present in 81.8% of dCCA cases (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Importantly, PNI has been recognized as a significant prognostic indicator affecting patients with resectable dCCA (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePeripheral nerves constitute a complicated tumor ecosystem comprising diverse cell populations, including Schwann cells (SCs), and various factors. Physical contact between malignant cells and SCs has been found to promote directed movement and invasion of cancer cells (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Fuji-Nishimura \u003cem\u003eet al\u003c/em\u003e. demonstrated that SCs facilitate colonization of pancreatic cancer in nerves by activating the epithelial-mesenchymal transition (EMT) pathway in tumor cells (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Recent research has illuminated that SCs could contribute to tumor progression by transitioning to a dedifferentiated state, analogous to their response to neurotrauma (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This reprogramming of SCs leads to the re-expression of glial fibrillary acidic protein (\u003cem\u003eGFAP\u003c/em\u003e), neural cell adhesion molecule 1 (\u003cem\u003eNCAM1\u003c/em\u003e), and L1 cell adhesion molecule (\u003cem\u003eL1CAM\u003c/em\u003e), which can drive the development of PNI (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Presently, the initiator and tumor-promoting effect of dedifferentiated Schwann cells (dSCs) in PNI-positive dCCA remain unclear. Consequently, a thorough comprehension of the cellular and molecular mechanism underlying neuromodulation of cancer progression is crucial for developing strategies for inhibiting tumor progression (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHerein, we employed the powerful technique of single-cell sequencing to profile PNI-positive dCCA and adjacent tissues, and identified two PNI-associated cellular components: \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells and \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs. We provided hitherto undocumented evidence that lactate in tumor hypoxic microenvironment could initiate \u003cem\u003eGFAP\u003c/em\u003e-dedifferentiation in SCs, and the latter enhanced dCCA tumor progression through upregulating high mobility group box 1 (\u003cem\u003eHMGB1\u003c/em\u003e). Taken together, our findings offer an exhaustive transcriptomic overview and elucidate the intercellular interaction between malignant cells and SCs in dCCA, revealing potential therapeutic vulnerabilities in PNI-positive dCCA.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell Transcriptomic Profiling Uncovered the Spectrum of Cell Populations in Human PNI-positive dCCAs\u003c/h2\u003e \u003cp\u003eTo comprehensively understand the tumor ecosystem in dCCA with PNI, we conducted single-cell RNA sequencing (scRNA-seq) on tumor and matched adjacent non-neoplastic tissues from two untreated PNI-positive dCCAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Detailed clinicopathological features of the study population are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Following quality control and filtering, single-cell transcriptome profiles were obtained for 24 715 cells. Eight primary cell types were determined informed by established marker genes, including epithelial cells (2 696, 10.9%), myeloid cells (4 212, 17.0%), lymphoid cells (13 161, 53.3%), endothelial cells (1 946, 7.9%), Schwann cells (SCs, 472, 1.9%), fibroblast (1 919, 45.6%), MKI67\u003csup\u003e+\u003c/sup\u003e cells (227, 0.9%), and smooth muscle cells (SMCs, 82, 0.3%, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Subsequently, we extracted all epithelial cells and identified 13 subclusters through reclustering analysis. Clusters 2 and 4 were considered normal epithelium and served as a normal reference for copy number variation (CNV) analysis due to their predominant distribution in adjacent noncancerous tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). A total of 1 203 malignant cells expressing high levels of \u003cem\u003eKRT19\u003c/em\u003e were inferred and further reclustered (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD illustrates the initial 21 cell cluster sources for all cells. Consistent with previous dCCA studies (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), non-malignant cells (excluding SMCs) exhibited inter- and intratumoral heterogeneity across different tissues. For instance, endothelial cells, epithelial cells, myeloid cells, and fibroblasts were heavily infiltrated in tumors, whereas lymphoid cells and SCs were predominantly found in adjacent biliary ductal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Moreover, to validate our findings, we deconvoluted bulk RNA-seq data from a larger cohort of paired CHOL and normal samples using CIBERSORTx (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) to deconvolute bulk RNA-seq data from a broader cohort of paired cholangiocarcinoma (CHOL) and normal samples. The relative abundance of endothelial cells, fibroblasts, and SCs in our samples aligned with estimates from the TCGA-CHOL dataset. However, epithelial cells and immune cells displayed discrepant patterns (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHighly heterogeneous hypoxic patterns of malignant cells and their contribution to the PNI-positive dCCA microenvironment\u003c/h3\u003e\n\u003cp\u003eTo characterize the tumor cell landscape in PNI-positive dCCA, malignant cells were subsequently clustered and divided into three primary subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). In alignment with previous findings in distal cholangiocarcinoma (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), malignant cells exhibited significant intra- and inter-tumor heterogeneity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The distinctive gene expression patterns within these three subpopulations are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. Cluster 0 was enriched for cells that highly expressed genes in the S100 family, such as \u003cem\u003eS100A4\u003c/em\u003e, \u003cem\u003eS100A10\u003c/em\u003e, and \u003cem\u003eS100A11\u003c/em\u003e. S100 protein family members have been commonly observed to be dysregulated in various tumors, including intrahepatic cholangiocarcinoma, and are critically implicated in carcinogenesis and cancer progression (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Cluster 1 was characterized by a prominent upregulation of \u003cem\u003eNEAT1\u003c/em\u003e and \u003cem\u003eMALAT1\u003c/em\u003e. These two adjacent long non-coding RNA (lncRNA) genes have been extensively documented to be involved not only in activating multiple oncogenic mechanisms but also in conferring resistance to chemotherapeutics (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). \u003cem\u003eTOP2A\u003c/em\u003e and \u003cem\u003ePTTG1\u003c/em\u003e, both of which were the marker genes of Cluster 2, have been previously implicated as oncogenes in a wide variety of human tumors (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Hypoxia is a ubiquitous property of most solid cancers and is strongly linked to tumor metastasis and invasion (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). We subsequently visualized the hypoxia statuses of malignant cells using cellular hypoxia predicting framework (CHPF) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Among these, most hypoxic cells were concentrated in cluster 1 (\u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) malignant cells, with fewer found in cluster 0 (\u003cem\u003eS100A4\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) and cluster 2 (\u003cem\u003eTOP2A\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Additionally, to explore the influence of hypoxia on the evolutionary dynamics of malignant cells in PNI-positive dCCA, Monocle2, and CytoTRACE were employed to perform unsupervised cell trajectory analysis, both of which revealed a similar differentiation pathway of malignant cells originating from hypoxic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA), consistent with the conclusion drawn by Zhang \u003cem\u003eet al\u003c/em\u003e. in glioblastoma (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Three cell states (S1-S3) were defined for pseudotime trajectory analysis based on Monocle2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). In terms of cellular status, hypoxic cells were primarily confined to S1 and S2 at the initial stage of differentiation, whereas hypoxic cells were predominantly concentrated in S3. Regarding cell clusters, \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells (cluster 1) dominated the S1 state, appearing at the earliest stage of pseudotime and exhibiting significantly higher stemness scores. We postulated that the high stemness of cluster 1 might be related to the hypoxic induction it faced, according to previous studies (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Correspondingly, \u003cem\u003eS100A4\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells (cluster 0) constituted the primary subcluster of the S3 state and were exclusively observed in the final stage of cell differentiation. Notably, \u003cem\u003eTOP2A\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells (cluster 2) spanned across both S2 and S3 states, suggesting the presence of two distinct cell substates within cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Taken together, these findings indicated an orchestrated differentiation process of dCCA cells during PNI. \u003cem\u003ePEAK1\u003c/em\u003e, a novel human pseudokinase, has recently been implicated in cancer pathogenesis (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). We observed that \u003cem\u003ePEAK1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells were positioned at the beginning of the major branch and aligned well with cluster 1. Similarly, metastasis scores and hepatic vascular invasion scores were predominantly observed at the onset of differentiation. These findings suggested that cluster 1 might represent a key cell type with high invasiveness in PNI-positive dCCA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo delve deeper into the relationship between hypoxia and the malignant properties of PNI-positive dCCA, hypoxia-related signature genes from the CancerSEA database (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and several hallmark gene sets, including EMT, IL2/STAT5, PI3K/AKT/mTOR, and KRAS signaling from the Molecular Signature Database (MSigDB), were manually curated (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Given gene set variation analysis (GSVA) to determine the activity score of each malignant cell, a significant positive correlation between hypoxia and the number of oncogenic pathway activities in PNI-positive dCCA was implied. The results showed a significant positive correlation between hypoxia and the number of oncogenic pathway activities in PNI-positive dCCA (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). Utilizing the previously inferred single-cell CNV spectrum, we observed that clusters 1 and 2 exhibited higher CNV levels than cluster 0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). In addition, the extent of CNV accumulation was correlated with the hypoxic status of cells. As exemplified by malignant cells derived from patient 01, hypoxic malignant cells displayed significantly higher CNV levels than normoxic malignant cells, indicative of a more malignant phenotype. In this respect, high-frequency CNV events were enriched in certain chromosomes, such as chr6, chr12, and chr15 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). We categorized all malignant cells into low and high groups based on CNV levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-E). The percentage of cells with a high degree of hypoxia within each malignant cell cluster demonstrated a positive correlation with the percentage of CNV-high cells in that cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). In addition, pathway enrichment analysis using GSVA revealed that MTORC1 signaling, MYC targets, E2F targets, and EMT pathways were enriched in the CNV-high group (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC). Overall, hypoxia and high CNV levels might be crucial for preserving the malignant characteristics of cluster 1. To summarize the transcriptomic features of malignant cells, we integrated meta-information regarding cell cluster, hypoxic state, CNV status, and predicted trajectories. The dynamic gene expression patterns observed divided the cellular developmental process into two distinct phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Correspondingly, the initial phase primarily comprised states S1 and S2, and there was a propensity for cluster 1 cells to transition to cluster 2 during this stage. This transition was accompanied by upregulation of the hypoxia-induced gene \u003cem\u003eVEGFA\u003c/em\u003e and the oncogenic driver \u003cem\u003eAKT\u003c/em\u003e, as well as enhanced signaling pathways associated with hypoxia response and epithelial cell migration. In contrast, cluster 1 cells in the second phase exhibited a greater propensity to transform into cluster 0 and subsequently progress toward the S3 state, characterized by heightened expression of \u003cem\u003eRPS15\u003c/em\u003e and a shift in energy metabolism towards aerobic respiration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eTo investigate the clinical implication of the malignant cell subtypes identified in our study, we estimated the proportion of epithelial cell subpopulations (including normal epithelial cells) within patient samples from the TCGA-CHOL cohort utilizing CIBERSORTx (Table S3). Only the abundance of cluster 1 malignant cells showed a significant correlation with reduced overall survival (OS; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI-J). We subsequently obtained similar results using GSE107943 as a validation dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Table S4). Furthermore, utilizing information on samples from the TCGA cohort containing patient PNI status, we discovered that cluster 1 malignant cells were significantly more abundant in CHOL with PNI than CHOL without PNI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK). These findings indicated that cluster 1 (\u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) malignant cells, characterized by hypoxia and high copy number variations, are associated with PNI progression in dCCA.\u003c/p\u003e\n\u003ch3\u003eDedifferentiated SCs play a significant role in dCCA perineural invasion\u003c/h3\u003e\n\u003cp\u003eSCs have been firmly established as a novel cell type within the tumor microenvironment, playing a specific and cancer-promoting role in PNI (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). We focused our analysis on SCs within the tumor microenvironment, performing unsupervised clustering on 472 cells, identifying five distinct subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Utilizing a marker gene list curated from the Tabula Sapiens portal (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and previous literature by Kastriti \u003cem\u003eet al\u003c/em\u003e. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), we observed that clusters 0 and 1 exhibited overexpression of myelinating SC (mSC) markers like \u003cem\u003eEGR2\u003c/em\u003e, \u003cem\u003eMPZ\u003c/em\u003e, and \u003cem\u003ePMP22\u003c/em\u003e. Cluster 2 displayed upregulation of well-defined non-myelinating SC (nmSC) markers such as \u003cem\u003eIGFBP5\u003c/em\u003e, \u003cem\u003eTAGLN2\u003c/em\u003e, \u003cem\u003eTPM1\u003c/em\u003e, and \u003cem\u003eA2M\u003c/em\u003e. Notably, cluster 4 preferentially expressed genes indicative of SC precursors (SCP): \u003cem\u003eCD69\u003c/em\u003e, \u003cem\u003eBTG1\u003c/em\u003e, \u003cem\u003eCD52\u003c/em\u003e, \u003cem\u003eCYBA\u003c/em\u003e, and \u003cem\u003eLTB\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Figure S3A). Among these clusters, \u003cem\u003eMPZ\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e mSCs (cluster 0), \u003cem\u003ePMP22\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e mSCs (cluster 1), and SCPs (cluster 4) were predominantly located in cancer-adjacent tissues. Conversely, nmSCs (cluster 2) and cluster 3 had a greater proportion of cells distributed within cancer tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC illustrates the unique transcriptomic signatures of all SC subsets identified in dCCA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo investigate the developmental pathways and potential roles of these distinct SC subclusters in dCCA with PNI, we first employed CytoTRACE to estimate the differentiation degree of each subcluster. As expected, the SCP cluster, representing multipotent embryonic progenitors for many neural cells (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), possessed the highest differentiation score (Figure S3B). Then, we reconstructed the SCs into a pseudotime trajectory using Monocle2, designating the SCP cluster as the starting point. Five distinct cell states (S1-S5) and a primary trajectory route were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). We observed that cluster 3 was positioned in close proximity to the differentiation starting site and characteristically expressed the dedifferentiation markers \u003cem\u003eGFAP\u003c/em\u003e and \u003cem\u003eNCAM1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Furthermore, we found that cluster 3 also upregulated the myelin gene \u003cem\u003eSOX2\u003c/em\u003e and the immature genes \u003cem\u003eNGFR\u003c/em\u003e and \u003cem\u003eL1CAM\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), aligning well with the reprogramming process of dedifferentiated SCs described by Jessen \u003cem\u003eet al\u003c/em\u003e. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Therefore, we classified cluster 3 SCs as dSC cells. Previous studies have indicated that both mSCs and nmSCs can contribute to cancer progression by transitioning to the dSC phenotype characteristic of repair SCs in cancer (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Our analysis further revealed that \u003cem\u003ePMP22\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e mSCs and certain nmSCs (specifically cluster S4) initially transitioned to cluster 0 (\u003cem\u003eMPZ\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e mSCs) during the dedifferentiation process. Notably, this transition occurred with minimal upregulation of immature genes, while \u003cem\u003eJUN\u003c/em\u003e expression increased but \u003cem\u003eSOX2\u003c/em\u003e remained relatively unchanged. Indeed, \u003cem\u003eSOX2\u003c/em\u003e is also a myeloid gene suppressor. In contrast, \u003cem\u003eSOX2\u003c/em\u003e expression became prominent during the dSC stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E). These findings suggest that the abandonment of myelin differentiation in dSCs may precede the activation of the immature phenotype, and different myeloid gene suppressors seem to act asynchronously. Overall, for PNI-positive dCCA, most SCs transition from SCPs to \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs, traversing an intermediate state (Cluster 0, \u003cem\u003eMPZ\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e mSCs). Ultimately, they may develop into \u003cem\u003ePMP22\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e mSCs or nmSCs. Our analysis provides a comprehensive ecological map and trajectory evolution of SCs in PNI-positive dCCA.\u003c/p\u003e \u003cp\u003eGene ontology (GO) analysis revealed that \u003cem\u003eMPZ\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e mSCs were significantly enriched in neuron apoptosis processes, neuron death, and tumor necrosis factor-mediated signaling pathways, possibly reflecting the damage response induced by cancer cell invasion (Figure S3C). Conversely, enriched GO terms for \u003cem\u003ePMP22\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e mSCs were associated with neural support and regeneration, including axonogenesis, axon development, and regulation of synapse maturation (Figure S3C). nmSCs were characterized by a high level of extracellular matrix similar to fibroblasts, while GO terms of \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs were enriched in cholesterol binding, lipid transfer activity, and phosphatidylcholine binding, indicating their high metabolic properties (Figure S3C). Finally, GO analysis of SCPs revealed their enrichment in pathways such as activation of the immune response, regulation of T cell activation, and neutrophil migration, suggesting a potential role in immune regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). To further elucidate the metabolic landscape of SCs in PNI-positive dCCA, scMetabolism was employed (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) to systematically quantify metabolic activities at single-cell resolution. We computed metabolic pathway activity scores for all 63 metabolic pathways annotated in scMetabolism and found that \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs exhibited the highest metabolic activity. Among these pathways, pyruvate metabolism, lactate metabolism, glycerolipid metabolism, and fatty acid biosynthesis were markedly activated in \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore the influence of each SC cluster on dCCA prognosis, CIBERSORTx was applied to determine the percentage of diverse SC types across the TCGA-CHOL samples (Table S5). High infiltration of \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs was associated with an inferior prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, Figure S3D). Similar results were obtained in the GSE107943 validation cohort (Figure S3E, Table S6). To investigate the contribution of \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs to the occurrence of PNI in dCCA, the TCGA-CHOL samples were sorted into PNI and non-PNI groups founded on the presence or absence of concomitant PNI. We observed that the PNI group displayed significantly higher infiltration of \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Additionally, 22 dCCA patients from the First Affiliated Hospital of Zhengzhou University were enrolled in our internal cohort (Zhengzhou-dCCA cohort). Hematoxylin and eosin (H\u0026amp;E) staining confirmed that all pathological sources were tumor tissues (Figure S4A). Immunohistochemistry (IHC) analysis demonstrated that the positive rates of \u003cem\u003eGFAP\u003c/em\u003e and \u003cem\u003eNCAM1\u003c/em\u003e proteins in the neural tissue of PNI samples were higher than those in non-PNI samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI, Table S7). Collectively, these data suggest that \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs possess high metabolic characteristics and play crucial roles in the PNI-positive microenvironment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInteractome landscape across\u003c/b\u003e \u003cb\u003eNEAT1\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003emalignant cells and\u003c/b\u003e \u003cb\u003eGFAP\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003edSCs in the PNI-related dCCA microenvironment\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo elucidate the crosstalk between \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells and \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs within the tumor microenvironment during PNI progression, we investigated intercellular communication by simulating ligand-receptor interactions using CellChat. A total of 116 pairs of interactions were identified across the four cell types we classified. Notably, interactions between \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells and \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs were more frequent (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). A similar pattern was observed in terms of the strength of intercellular interactions (Figure S5A). These results underscore the critical roles of \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells and \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs in PNI-positive dCCA. Subsequently, we utilized CellChat\u0026rsquo;s pattern recognition to identify major secretory signaling events of various cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). When \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells served as the signal source and \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs as the signal input, the \u003cem\u003eCDF15\u003c/em\u003e-\u003cem\u003eTGFBR2\u003c/em\u003e interaction exhibited the highest interaction score (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Previous studies have demonstrated that inactivation of the \u003cem\u003eTGFBR2\u003c/em\u003e gene leads to uneven and severely underdeveloped dedifferentiated SC invasion in mice (\u003cem\u003ein vivo\u003c/em\u003e), hindering their involvement in the bridge regeneration process after nerve injury (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). We also noted that the \u003cem\u003eCDF15\u003c/em\u003e gene was predominantly expressed in \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells, while \u003cem\u003eTGFBR2\u003c/em\u003e was generally distributed across all SC types (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Conversely, when \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs sent ligands to \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells, the primary interaction occurred through the \u003cem\u003eBTC\u003c/em\u003e-\u003cem\u003eEGFR\u003c/em\u003e pathway. The role of \u003cem\u003eEGFR\u003c/em\u003e in cancer progression and as a therapeutic target in various human malignancies, including cholangiocarcinoma, lung cancer, colon cancer, and breast cancer, has been well-established (\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Analyzing the receptor-ligand expression distribution, we found that BTC was almost exclusively expressed in \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs, while \u003cem\u003eEGFR\u003c/em\u003e was predominantly expressed by \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells. Therefore, the \u003cem\u003eBTC\u003c/em\u003e-\u003cem\u003eEGFR\u003c/em\u003e interaction pair might represent a characteristic mode of communication between \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs and \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D).\u003c/p\u003e \u003cp\u003eTo investigate the significance of the \u003cem\u003eNEAT1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;malignant cell-\u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSC interaction within the tumor microenvironment (TME), we utilized SCENIC (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) to decipher the gene regulatory network (GRN) of these cell types. The GRN differed among the subtypes of both malignant cells and SCs (Figure S5B-C). We identified four key genes in the GRN of \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells: \u003cem\u003eSREBF2\u003c/em\u003e, \u003cem\u003eATF3\u003c/em\u003e, \u003cem\u003eRFX2\u003c/em\u003e, and \u003cem\u003eJUN\u003c/em\u003e. These genes have previously been shown to be upregulated in damaged neurons and regulate oxidative stress during the dedifferentiation of neighboring SCs (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). \u003cem\u003eJUN\u003c/em\u003e is known to control myelin cell dedifferentiation and the activation of repair programs (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Conversely, multiple oncogenic transcription factors, including \u003cem\u003eETS1\u003c/em\u003e, \u003cem\u003eEP300\u003c/em\u003e, \u003cem\u003eSMAD4\u003c/em\u003e, and \u003cem\u003eELK4\u003c/em\u003e, were upregulated in \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs (Figure S5B-C). Jin \u003cem\u003eet al\u003c/em\u003e. reported that tumor-derived extracellular vesicles promote renal cell carcinoma invasion and metastasis by transferring \u003cem\u003eMALAT1\u003c/em\u003e facilitating the binding of the transcription factor \u003cem\u003eETS1\u003c/em\u003e and the \u003cem\u003eTFCP2L1\u003c/em\u003e promoter (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Interestingly, \u003cem\u003eMALAT1\u003c/em\u003e is one of the genes that characterize \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells. In conclusion, our data highlight the close communication between \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells and \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs within the PNI-associated dCCA microenvironment and identify potential transcription factor candidates for further investigation.\u003c/p\u003e\n\u003ch3\u003eHypoxia induces lactate secretion from cancer cells and further promotes SC dedifferentiation\u003c/h3\u003e\n\u003cp\u003ePrevious research demonstrated that pancreatic cancer cell supernatants under hypoxic conditions can induce \u003cem\u003eGFAP\u003c/em\u003e activation in human SCs (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Notably, hypoxia is also a hallmark feature of \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells. To investigate the mechanism of SC dedifferentiation induced by hypoxic cells, we initially cultured CCLP1 and HUCCT1 cell lines under hypoxic conditions \u003cem\u003ein vitro\u003c/em\u003e to simulate the \u003cem\u003ein vivo\u003c/em\u003e hypoxic TME. After a 48-h incubation under either normoxic or hypoxic conditions, \u003cem\u003eHIF-α\u003c/em\u003e levels were detected via Western blot analysis to assess the successful induction of hypoxic stress in the cancer cells. The results indicated a significant enhancement of \u003cem\u003eHIF-1α\u003c/em\u003e expression under hypoxic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), confirming the effectiveness of our hypoxia modeling. Subsequently, we stimulated ipNF95.6 human SC cells with the modeled cancer cell supernatants to evaluate the activation of SCs by the hypoxic cholangiocarcinoma cell microenvironment. A significant increase in GFAP protein expression was observed when ipNF95.6 cells were exposed to the supernatant of the hypoxia group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Given that cancer cells consume substantial amounts of oxygen and nutrients, secreting excess lactate (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), and the high lactate metabolic activity of dedifferentiated cells in our previous analysis, we sought to determine whether SC dedifferentiation was related to lactate within the tumor's hypoxic microenvironment. We initially measured lactate levels in the cell supernatant of hypoxic cancer cells. Our findings revealed a significant elevation of lactate levels within the supernatant of hypoxic cancer cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Similarly, we measured lactate levels in 22 CHOL tissues from the Zhengzhou-CHOL cohort, comparing the GFAP protein-negative and GFAP protein-positive nerve groups. We observed significantly higher lactate levels in the GFAP protein-positive nerve group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). For NCAM1 protein, there was a trend towards higher lactate content in the NCAM1 protein-positive nerve group, although these results were not statistically significant. Furthermore, we categorized all SCs from previous single-cell data into high and low lactate metabolism groups according to the median lactate metabolic activity score. We found that the expression of multiple dedifferentiation-related SC markers, including \u003cem\u003eL1CAM\u003c/em\u003e, \u003cem\u003eJUN\u003c/em\u003e, \u003cem\u003eNCAM1\u003c/em\u003e, \u003cem\u003eGFAP\u003c/em\u003e, and \u003cem\u003eNGFR\u003c/em\u003e, was increased in the high lactate metabolism group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). To examine the impact of lactate on SC dedifferentiation, we conducted a series of experiments. Referencing a previous study (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), we established a gradient lactate concentration (0, 10, 20, 40, 80, and 160 mM) to determine the optimal lactate concentration. SCs exposed to different lactate levels were cultured for 8 h, and their viability was assessed. The results indicated a dramatic decrease in SC viability at a lactate concentration of 20 mM. Consequently, we selected a lactate concentration of 10 mM for subsequent experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Next, we analyzed multiple representative dedifferentiation-related genes, among which the mRNA and protein levels of \u003cem\u003eNCMA1\u003c/em\u003e, \u003cem\u003eGFAP\u003c/em\u003e, and \u003cem\u003eSOX2\u003c/em\u003e were noticeably upregulated in lactate-induced SCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG-H). Additionally, we obtained cross-species validation in RSC96 (a rat Schwann cell line, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI), indicating that the evolutionary process of lactate-induced dedifferentiation in SCs might be conserved. Collectively, these findings indicate that lactate produced by hypoxic cancer cells promotes the dedifferentiation of SCs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCancer cell-derived lactate upregulates\u003c/b\u003e \u003cb\u003eHMGB1\u003c/b\u003e \u003cb\u003ein SCs, which further promotes the carcinogenic behavior of cholangiocarcinoma cells\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eHMGB1\u003c/em\u003e was initially reported to be released from lipopolysaccharide-stimulated macrophages and to function as a pro-inflammatory factor in sepsis (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). More recent studies have demonstrated that stromal cells, such as tumor-associated macrophages, upregulate intracellular \u003cem\u003eHMGB1\u003c/em\u003e expression upon lactate stimulation, thereby promoting cancer progression (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Interestingly, our single-cell data revealed that \u003cem\u003eHMGB1\u003c/em\u003e was generally upregulated in \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs, and the percentage of \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e cells within SCnn cells correlated with the percentage of high \u003cem\u003eHMGB1\u003c/em\u003e-expressing cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Figure S6A). Furthermore, a protein-protein interaction (PPI) network was constructed utilizing the STRING database v12.0, linking \u003cem\u003eHMGB1\u003c/em\u003e with 14 SC dedifferentiation-related genes. The PPI network indicated an interaction between \u003cem\u003eHMGB1\u003c/em\u003e and \u003cem\u003eGFAP\u003c/em\u003e and \u003cem\u003eJUN\u003c/em\u003e (Figure S6B). To further investigate the relationship between SC dedifferentiation and \u003cem\u003eHMGB1\u003c/em\u003e expression, we performed IHC in the Zhengzhou-CHOL cohort (Figure S6C). The images revealed higher \u003cem\u003eHMGB1\u003c/em\u003e IHC scores in GFAP and NCAM1 proteins in the neural tissue of positive groups were higher than those of negative groups, although the latter showed no statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Additionally, we found that HMGB1 protein was significantly upregulated after stimulation of ipNF95.6 cells with hypoxia-treated cancer cell supernatants (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). To determine if \u003cem\u003eHMGB1\u003c/em\u003e expression in Schwann cells was similarly linked to lactate secreted in the tumor hypoxic microenvironment, immunofluorescence experiments demonstrated an increase in the cytoplasmic level of \u003cem\u003eHMGB1\u003c/em\u003e in lactate-treated ipNF95.6 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). To investigate the role of \u003cem\u003eHMGB1\u003c/em\u003e in SC dedifferentiation and its impact on tumor progression, we introduced glycyrrhizin (1 nM) to inhibit \u003cem\u003eHMGB1\u003c/em\u003e expression in subsequent protein immunoblotting and immunofluorescence experiments (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Our findings revealed a significant elevation of HMGB1 protein levels in lactate-stimulated SCs, which was effectively inhibited by glycyrrhizin (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, Figure S6D). Next, we sought to understand whether SCs stimulated with lactate promoted tumor progression through \u003cem\u003eHMGB1\u003c/em\u003e. Co-culture experiments with lactate-induced SCs demonstrated accelerated cell migration and invasion in both types of cancer cells. However, glycyrrhizin reversed the lactate-induced effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-G). To further evaluate the functional role of lactate-induced dedifferentiated Schwann cells in CCA progression, we conducted \u003cem\u003ein vivo\u003c/em\u003e experiments using xenograft mice. Mice injected with a mixture of lactate-stimulated SCs and CCLP1 cells exhibited larger tumor volumes. Notably, glycyrrhizin attenuated the tumor growth-promoting effect of lactate-stimulated SCs through \u003cem\u003eHMGB1\u003c/em\u003e inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH-I). Considering that the nuclear protein HMGB1 is released in response to diverse stimuli, including lactate (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), we focused on the expression level of \u003cem\u003eHMGB1\u003c/em\u003e within tumor cells after co-culture with Schwann cells. Lactate-treated SCs elevated the level of \u003cem\u003eHMGB1\u003c/em\u003e within cancer cells, while glycyrrhizin inhibited this elevation (Figure S6E). Collectively, these results suggest that SCs enhance the invasion and migration of cancer cells through lactate-induced upregulation of \u003cem\u003eHMGB1\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAt present, dCCA, a subtype of CCA, remains a highly lethal disease despite significant advancements in scientific understanding and clinical management (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In recent years, numerous studies have been conducted on the molecular pathogenesis of CCA specially for iCCA, yet the unraveling of the molecular complexity of dCCA remains limited, and there are no approved targeted therapies with demonstrated clinical benefit. PNI, a common pathological feature in dCCA, is strongly associated with postoperative recurrence and poor prognosis (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Several studies have highlighted the pivotal role of dSCs in PNI and cancer progression (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). A deep appreciation for the cellular ecosystem of PNI-associated dCCA and the potential molecular mechanisms underlying the contribution of dSCs to PNI remains an unmet clinical need. In our study, we utilized scRNA-seq to comprehensively map the transcriptomic landscape of human PNI-positive dCCA, unveiling novel cell-cell communications between dCCA cells and dSCs at single-cell resolution.\u003c/p\u003e \u003cp\u003eThrough scRNA-seq analysis, we identified multiple distinct cell types within PNI-positive dCCAs. Lymphoid cells predominated in PNI-positive dCCAs, accounting for over 30% of all cells, followed by myeloid cells and epithelial cells (both malignant and normal epithelium). The distribution of individual cell subsets within a single sample varied significantly, demonstrating substantial inter-tumor heterogeneity. scRNA-seq profiling enabled the definition of three distinct malignant subtypes. These three malignant subtypes exhibited specific differentially expressed genes (DEGs) and potential transcription factors (TFs). Importantly, we observed that an accumulation of \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells was related to poorer clinical outcomes and the development of PNI in patients with dCCA, suggesting the tumor-promoting characteristics of \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells. Notably, we found that \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells displayed a highly hypoxic profile. Hypoxia-induced NEAT1 has been reported to be mediated by HIF-2α transcriptional activity (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). CNV-based analysis revealed a significantly higher proportion of CNV-high malignant cells within \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells than other malignant cell subtypes, confirming the malignant nature of PNI-associated malignant cells. Our research employed pseudotemporal trajectory analysis to identify three distinct malignant cell states and further characterized their developmental dynamics. Among these states, \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells might represent an earlier stage of differentiation in dCCA cells. We identified several \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cell-associated genes, many of which are related to hypoxic and oncogenic signaling pathways, such as \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eAKT\u003c/em\u003e, \u003cem\u003eJUN\u003c/em\u003e, and \u003cem\u003eKRAS\u003c/em\u003e. Previous studies have reported that \u003cem\u003eJUN\u003c/em\u003e promotes de-differentiation of SCs after neural injury by inhibiting \u003cem\u003eP0\u003c/em\u003e, \u003cem\u003emyelin basic protein genes\u003c/em\u003e, and \u003cem\u003eKROX20\u003c/em\u003e (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Interestingly, \u003cem\u003eJUN\u003c/em\u003e was also a predicted regulator of \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells. This suggests the possibility of \u003cem\u003eJUN\u003c/em\u003e-mediated cellular communication between these malignant cells and dSCs, which warrants further investigation. Additionally, upregulated \u003cem\u003eMALAT1\u003c/em\u003e in \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells was associated with synapse formation and neuronal cell survival (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e), potentially resulting from the close interaction between \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells and dSCs predicted at the single-cell level. In conclusion, our findings highlight that \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells, characterized by hypoxia, are a representative group of malignant cells in PNI-positive dCCA.\u003c/p\u003e \u003cp\u003eAs major constituent cells of nerves, SCs have been demonstrated to promote tumor growth and play a pivotal role in PNI across multiple tumor types. In this study, we provided hitherto undocumented evidence of five distinct SC subtypes in human dCCA and its adjacent tissues. Notably, nmSCs and \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs exhibited a higher proportion of cells within dCCA tissues, while SCPs, \u003cem\u003ePMP22\u003c/em\u003e\u0026thinsp;+\u0026thinsp;mSCs, and \u003cem\u003eMPZ\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e mSCs were predominantly found in the adjacent tissues, highlighting the heterogeneity of the neurological tissue microenvironment in dCCA. Our analysis revealed that \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs expressed high levels of myeloid and immature SC genes, suggesting they might be a biochemically and metabolically active subpopulation of SCs. Subsequently, we verified that cholangiocarcinoma cell-derived lactate is a metabolite that induces and maintains \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSC dedifferentiation. It has been reported that monocarboxylate transporter protein (\u003cem\u003eMCT\u003c/em\u003e) is highly expressed in perineuronal cells and facilitates lactate uptake as its preferred energy metabolite (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). The dependence of peripheral nerve function on lactate metabolism was further emphasized in a study by Morrison \u003cem\u003eet al\u003c/em\u003e., where \u003cem\u003eMCT1\u003c/em\u003e deficiency impeded nerve regeneration after peripheral nerve injury in mice (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Importantly, pseudotemporal trajectory analysis and survival analysis indicated that \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs, following SCPs, are the most na\u0026iuml;ve and represent a malignant SC population within the PNI-positive dCCA neural microenvironment, potentially originating from \u003cem\u003eMPZ\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e mSCs. Therefore, the \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs subpopulation may serve as a promising therapeutic target for dCCA patients with concomitant PNI.\u003c/p\u003e \u003cp\u003eHMGB1, a representative injury-associated molecule, has been implicated in various pathological processes, including neurodegenerative diseases, autoimmunity, and cancer progression (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Peripheral nerve injury can induce \u003cem\u003eHMGB1\u003c/em\u003e expression through the proliferation of SCs and infiltration of macrophages within nerves (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). \u003cem\u003eHMGB1\u003c/em\u003e expression is significantly elevated in perihilar cholangiocarcinoma tissues (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e) and is associated with poor prognosis, lymphatic invasion, and direct involvement in cholangiocarcinoma proliferation and angiogenesis (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Recent studies have demonstrated that lactate stimulates macrophage M2 polarization and secretes \u003cem\u003eHMGB1\u003c/em\u003e, thereby promoting glioma cell invasion (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). IHC staining also confirmed that \u003cem\u003eGFAP\u003c/em\u003e and \u003cem\u003eNCAM\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e peripheral nerves express high levels of \u003cem\u003eHMGB1\u003c/em\u003e internally, which prompted us to investigate whether SCs also act as a lactate-induced \"\u003cem\u003eHMGB1\u003c/em\u003e reservoir,\" contributing to neural infiltration by dCCA cells. In this study, we found that cholangiocarcinoma cell-derived lactate stimulated the dedifferentiation of SCs and significantly induced \u003cem\u003eHMGB1\u003c/em\u003e expression in \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dSCs, enhancing malignancy. It is widely believed that \u003cem\u003eHMGB1\u003c/em\u003e may directly contribute to tumor cell metastasis by modifying extracellular matrix components and regulating cell adhesion properties (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e) or enhance tumor cell progression by inducing melanoma inhibitory activity (\u003cem\u003eMIA\u003c/em\u003e) (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Cellular immunofluorescence confirmed a significant upregulation of \u003cem\u003eHMGB1\u003c/em\u003e in cholangiocarcinoma cells following co-culture with SCs that exocrine \u003cem\u003eHMGB1\u003c/em\u003e, suggesting its potential role in exacerbating their malignant behavior.\u003c/p\u003e \u003cp\u003eTo sum up, our study presents a uniquely matched set of transcriptomic landscapes within the tumor microenvironment of paraneoplastic and PNI-positive dCCA tumor samples, offering a valuable resource for elucidating SC diversity in PNI-positive dCCA. This study also highlights the intra-tumor crosstalk between PNI-associated malignant cells and dedifferentiated SCs. Future research is warranted to corroborate the molecular mechanisms underlying dCCA PNI, and our dataset can serve as a valuable tool for designing targeted therapeutics against PNI-positive tumors.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003ePatients and clinical sample collection\u003c/h2\u003e \u003cp\u003eTwo patients with dCCA who did not receive preoperative chemotherapy or radiotherapy participated in this study. Informed consent was obtained from all participants, who were requested to donate their tumor tissues and corresponding peri-tumor tissues for scientific research. The study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (ethical approval number: 2018-KY-83). Tissue samples were transported on ice and processed within 30 min of acquisition.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003escRNA-seq and data analysis of cholangiocarcinoma tissue\u003c/h3\u003e\n\u003cp\u003eSingle-cell suspensions were prepared for each sample. Cell viability was ensured to be above 70%, and the cell concentration was adjusted to 300\u0026ndash;600 cells/\u0026micro;L. scRNA-seq was performed using the 10X Genomics Chromium Single Cell 3' platform following the manufacturer's instructions. The generated count matrices were converted to a Seurat object using the Seurat package (version 4.4.0) (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). Cells expressing fewer than 200 genes or with mitochondrial reads exceeding 40% were excluded from downstream analysis. Batch effect correction was conducted using the Harmony package (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e), and the filtered gene-barcode matrices were normalized using the LogNormalize method. The top 3 000 highly variable genes for principal component analysis (PCA) were identified using the 'FindVariableFeatures' function. The top 30 principal components (PCs) were then selected for Uniform Manifold Approximation and Projection (UMAP) visualization of the cells. For cell clustering, the 'FindClusters' function was employed at a resolution of 0.3. Subgroup cell clusters were analyzed by selecting the top 30 PCs and clustering at various resolutions, which were determined through visual inspection.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDistinguish malignant and non-malignant epithelial cells based on inferred CNVs\u003c/h2\u003e \u003cp\u003eInitial CNVs were estimated using the inferCNV package (version 1.12.0), as previously described (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). To minimize the impact of genes with extreme expression, the expression values were re-standardized and restricted to the range [-2,2]. For each cell, the mean of the squared CNVi (CNV of the ith window) across the genome was calculated as the CNV signal. Additionally, the CNV correlation values were calculated by correlating the CNV profile of a single cell with the average CNVi profile of the top 5% of single cells with the highest CNV scores. Epithelial cells with a CNV signal above 0.225 and a CNV correlation above 0.45 were classified as malignant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of high-confidence hypoxic cells using the CHPF model\u003c/h2\u003e \u003cp\u003eCHPF (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) is an open-source modeling framework designed to predict cellular hypoxia status. The CHPF script was executed in Python (version 3.11.5). The single-cell expression profile and seven pre-selected hypoxia-related gene sets served as input files for the construction of the prediction model. The final formula is provided below:\u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\sum\\:}_{i=1}^{n}{W}_{i}\\left(x\\right){f}_{i}\\left(x\\right)}{{\\sum\\:}_{i=1}^{n}{W}_{i}\\left(x\\right)}\\)\u003c/span\u003e\u003c/span\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003cp\u003eCells with \u003cem\u003eP\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;0.5 were considered as hypoxic cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePseudotime analysis by Monocle\u003c/h2\u003e \u003cp\u003ePseudo-time analysis and transcriptome dynamic analysis along the pseudo-time trajectory were conducted using Monocle2 (version 2.26.0) (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e) with the default parameters recommended by the developer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDeconvolution analysis\u003c/h2\u003e \u003cp\u003eWe employed CIBERSORTx, a deconvolution analysis tool, to investigate gene expression within the tumor microenvironment. Our analysis focused on the 10X single-cell RNA sequencing data, specifically looking for DEGs between epithelial cell subtypes and SC subtypes. These genes were used to create a signature matrix. To deconvolute the bulk RNA-sequencing data, we employed two separate reference sources: 1) data from the TCGA-CHOL cohort within The Cancer Genome Atlas (TCGA); and 2) an RNA-sequencing dataset (GSE107943) downloaded from the Gene Expression Omnibus (GEO) database. These datasets served as the mixture files for CIBERSORTx analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eH\u0026amp;E staining and IHC assay\u003c/h2\u003e \u003cp\u003e22 cases of dCCA tissues that underwent pancreaticoduodenectomy were obtained from the First Affiliated Hospital of Zhengzhou University between 2021 and 2024. Tissues were fixed with 4% paraformaldehyde, embedded in paraffin, and cut into 4 \u0026micro;m sections. For H\u0026amp;E staining, the sections were stained with hematoxylin (BA4097, BaSo Diagnostics Inc., Zhuhai, China) for 5 min and eosin (BA4098, BaSo Diagnostics Inc.) for 3 min.\u003c/p\u003e \u003cp\u003eFor IHC assay, 4 \u0026micro;m thick sections were deparaffinized, rehydrated, and blocked. Primary antibodies were incubated at 4\u0026deg;C overnight, followed by incubation with goat anti-mouse IgG H\u0026amp;L HRP (1:4 000, ab205718, Abcam, Boston, USA) at room temperature for 2 h. The primary antibodies used in this study included GFAP (1:5 000, SA00001-1, Proteintech, Wuhan, China), NCAM1 (1:5 000, 14255-1-AP, Proteintech), and HMGB1 (1:400, 66525-1-Ig, Proteintech). The staining extent score (\u0026lt;\u0026thinsp;25%, score\u0026thinsp;=\u0026thinsp;1; 25\u0026ndash;50%, score\u0026thinsp;=\u0026thinsp;2; 50\u0026ndash;75%, score\u0026thinsp;=\u0026thinsp;3; \u0026gt;75%, score\u0026thinsp;=\u0026thinsp;4) and staining intensity (negative, score\u0026thinsp;=\u0026thinsp;0; weak, score\u0026thinsp;=\u0026thinsp;1; moderate, score\u0026thinsp;=\u0026thinsp;2; strong, score\u0026thinsp;=\u0026thinsp;3) were assessed using ImageJ software 1.46r. IHC results was scored by multiplying the staining extent score by the intensity score. All H\u0026amp;E stained and IHC sections were scanned with a Pannoramic MIDI II scanner (3D HISTECH Ltd., Hungary).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and treatment\u003c/h2\u003e \u003cp\u003eCCLP1 (JNO-H0653) and HUCCT1 (BNCC337995) human cholangiocarcinoma cell lines were obtained from Jennio Biotech Co., Ltd. (Guangzhou, China) and Beina Chuanglian Biotechnology Institute (Beijing, China), respectively. Human ipNF95.6 SCs (CTCC-001-0379, Meisen CTCC, Panan, China) and rat RSC96 SCs (CL-0199, Pricella Biotechnology Co., Ltd., Wuhan, China) were maintained in our laboratory. All cells were cultured in DMEM (12100, Solarbio, Beijing, China) medium supplemented with 10% fetal bovine serum (C04001-500, VivaCell, Shanghai, China). The normoxia group cells were cultivated at 37\u0026deg;C in a 5% CO\u003csub\u003e2\u003c/sub\u003e humidified incubator (Galaxy 170R, Eppendorf, Hamburg, Germany). For hypoxia induction, cells were transferred to a tri-gas incubator (Galaxy 48R, Eppendorf) and incubated under 1% O\u003csub\u003e2\u003c/sub\u003e, 5% CO\u003csub\u003e2\u003c/sub\u003e, and 94% N\u003csub\u003e2\u003c/sub\u003e for 48 h prior to commencing subsequent experiments.\u003c/p\u003e \u003cp\u003eA non-contact coculture system was established using 24-well plates containing 0.4\u0026micro;m polyethylene terephthalate membrane filters (Corning, NY, USA) to separate the lower and upper chambers. ipNF95.6 cells, subjected to various treatments, were seeded in the upper chamber at a density of 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/mL. CCLP1 or HUCCT1 cells were then inoculated in the lower chamber at a density of 1.5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/mL. Following a 48-h incubation period, cholangiocarcinoma cells were harvested for subsequent experiments.\u003c/p\u003e \u003cp\u003eTo stimulate lactate production, SCs were cultured for 8 h with increasing concentrations of lactate (0, 10, 20, 40, 80, and 160 mM; L1750, Merck, NJ, USA).\u003c/p\u003e \u003cp\u003eGlycyrrhizin (B20417-20mg, Yuanye Bio-Technology, Shanghai, China), a direct inhibitor of HMGB1, was added to SCs (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) at a concentration of 1 nM in conjunction with lactate to suppress HMGB1 expression in these cells.\u003c/p\u003e \u003cp\u003eThe supernatant of cancer cells cultured under normoxic or hypoxic conditions was collected by centrifuging at 500\u0026times;g for 10 min, followed by a second centrifugation at 2 000\u0026times;g for 20 min. After any necessary pre-treatments, SC supernatant was collected using the same procedure. The collected supernatants were stored at -80\u0026deg;C and used within one month.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCell\u0026ndash;cell interaction analysis\u003c/h2\u003e \u003cp\u003eIntercellular communication between malignant and SC types was investigated using the CellChat package (version 1.6.1) (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). The Seurat-normalized data was transformed into a CellChat object using the 'createCellChat' function. Subsequently, the 'computeCommunProbPathway' function was employed to infer intercellular communication at the signaling pathway level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSCENIC analysis\u003c/h2\u003e \u003cp\u003eSCENIC (version 1.3.1) was utilized to evaluate the transcriptional activity of malignant and SCs (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). SCENIC was implemented in R using the motif databases of RcisTarget and GRNboost (corresponding to GENIE3 1.20.0, AUCell 1.22.0, and RcisTarget 1.18.2). Raw UMI counts served as input for the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot assay\u003c/h2\u003e \u003cp\u003eThe total proteins were extracted with RIPA buffer (R0010, Solarbio). Proteins were separated by 10% SDS-PAGE and transferred to nitrocellulose membranes. The membranes were incubated with primary antibodies at 4\u0026deg;C overnight, and specific binding of the primary antibodies was detected with peroxidase-labeled goat anti-mouse (1:4 000, SA00001-1) or goat anti-rabbit (1:4 000, SA00001-2) secondary antibodies (Proteintech). The following primary antibodies were used: HIF1A (1:4 000, ab51608, Abcam), GFAP (1:5 000, 60190-1-Ig, Proteintech), NCAM1 (1:5 000, 60238-1-Ig, Proteintech), SOX2 (1:1 000, #2748, CST, Boston, USA) and HMGB1 (1:3 000, 66525-1-Ig, Proteintech) and β-actin (1:40 000, 66009-1-Ig, Proteintech).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLactate measurement\u003c/h2\u003e \u003cp\u003eThe lactate concentrations in cells or tissues were determined by an L-Lactate Assay Kit (A019-2-1, Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Samples were prepared according to the manufacturer\u0026rsquo;s instruction and the lactate levels of the samples were calculated by measuring the absorbance at 530 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCell cytotoxicity experiment\u003c/h2\u003e \u003cp\u003eFor the thiazolyl blue tetrazolium bromide (MTT) assay, MTT (M5655, Sigma, Shanghai, China) was dissolved in DMEM at a concentration of 5 mg/mL. Cells were incubated with MTT for 4 h at 37\u0026deg;C under 5% CO\u003csub\u003e2\u003c/sub\u003e. After removing the MTT, the formed MTT-formazan crystal was dissolved in DMSO (150 \u0026micro;L/well). Absorbance at 490 nm was measured using a microplate reader (Spark, Tecan, Switzerland). The results were expressed as the percentage change in absorbance compared to untreated control cells, which were set to 100%. Data represent the average of triplicate measurements from three independent experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eReal‑time quantitative polymerase chain reaction (RT‑qPCR)\u003c/h2\u003e \u003cp\u003eTotal RNA from cells was extracted using TriQuick Reagent (Solarbio). cDNA was synthesized using NovoScriptPlus All-in-one 1st Strand cDNA Synthesis SuperMix (E047-01B, Novoprotein Scientific Inc., Shanghai, China). RT-qPCR was conducted in a 20 \u0026micro;L reaction volume containing forward and reverse primers, cDNA, and NovoStart SYBR qPCR SuperMix Plus (E096-01A, Novoprotein Scientific Inc.). All primers were synthesized by Sangon Biotech and normalized to GAPDH. RNA folding changes were quantified using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. The primer sequences are listed in Table S8.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eImmunofluorescence (IF) staining\u003c/h2\u003e \u003cp\u003eCell climbing slices were sterilized with 75% alcohol, air-dried in 24-well plates, and inoculated with malignant or SCs. After that, cells were fixed with 4% paraformaldehyde, permeabilized with 0.2% Triton X-100 for 15 min at 4\u0026deg;C, and blocked with 5% bovine serum albumin. Primary HMGB1 antibody (1:200, 66525-1-Ig, Proteintech) was added overnight at 4\u0026deg;C. Cells were then incubated with goat anti-mouse IgG (H\u0026amp;L) secondary antibody (1:400, Alexa Fluor 488, GB25301, Servicebio, Wuhan, China) or (1:200, Alexa Fluor 594, AB0152, Abways, Shanghai, China) for 2 h at room temperature, protected from light, and counterstained with DAPI for 10 min. Images were captured using a DM4B microscope system (Leica, Wetzlar, Germany).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eWound healing assay\u003c/h2\u003e \u003cp\u003eA coculture system was employed for the scratching experiment. Following coculture completion, the upper chamber was removed, and the cells in the lower chamber were rinsed with phosphate-buffered saline (PBS). Cells were maintained in culture until reaching 90% confluence. Two lines were then scratched using a 200 \u0026micro;L pipette tip. Nonadherent cells were washed away twice with PBS and incubated in serum-free DMEM for 24 h. Microscopic observation and photography were performed at 0 and 24 h. The ImageJ software (Version 1.6.0) was utilized to analyze the wound healing rate by quantifying the wound closure area.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCell invasion assay\u003c/h2\u003e \u003cp\u003eA cell invasion assay was conducted using 24-well plates equipped with 8-\u0026micro;m pore polycarbonate membrane inserts (Corning, NY, USA). A 200 \u0026micro;L cell suspension (serum-free DMEM) was seeded in the upper chamber, and Medium (600 \u0026micro;L) containing 10% FBS was added to the lower chamber. Following incubation at 37\u0026deg;C for 48 h, non-migratory cells on the top surface of the inserts were gently removed with cotton swabs. Subsequently, the inserts were fixed in 4% paraformaldehyde for 15 min, and the cells were stained with 0.2% crystal violet. The number of cells that invaded the membrane was quantified under a light microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eTumorigenicity assay\u003c/h2\u003e \u003cp\u003eTumor xenografts were established in 5-week-old BALB/c nude mice obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd (Beijing, China). The mice were maintained in specific pathogen-free (SPF) condition with a 12-h light/dark cycle. After acclimatization for one week, 5\u0026times;10\u003csup\u003e6\u003c/sup\u003e CCLP1 cells mixed with 2\u0026times;10\u003csup\u003e6\u003c/sup\u003e ipNF95.6 cells were injected subcutaneously on the right flank of the mice. ipNF95.6 cells were pretreated with PBS, lactate, or lactate combined with glycyrrhizin for over three generations. Following inoculation, tumor volume was measured every three days and calculated using the formula V\u0026thinsp;=\u0026thinsp;ab\u003csup\u003e2\u003c/sup\u003e/2, where a is the long diameter and b is the short diameter. All animal experiments were approved by the Laboratory Animal Management Committee of Zhengzhou University.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe Shapiro-Wilk test was used to assess the normality of each distribution. Student's \u003cem\u003et\u003c/em\u003e-test or the Wilcoxon rank-sum test was employed to compare two groups, while ANOVA followed by Tukey's multiple comparisons test was used for multiple group comparisons. Data were analyzed using GraphPad Prism (version 8.0.1.244, GraphPad Software Inc., San Diego, USA). Kaplan-Meier curves were constructed and analyzed using the log-rank test. All data were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eContributions\u003c/h2\u003e \u003cp\u003eZ.Z., X.S., and W.Z. conceived the project and designed the experiments. Z.Z. and C.Z. performed this experimental work and draft the manuscript. Z.Z. led the data analysis in collaboration with J.S. and X.S. X.S., Z.Z., and K.H. performed the HE and IHC experiments, and assessed all the histology in this study. H.T. and E.L. contributed with critical reagents and materials. K.C., C.J., and R.F. provided useful suggestions for this manuscript.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe greatly appreciate the donations of samples from enrolled patients and help with sample collection by clinical staff at the Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital of Zhengzhou University. We thank the Laboratory Animal Center of Zhengzhou University (Henan Institute of Medical and Pharmaceutical Sciences, Academy of Medical Science, Zhengzhou University) for their assistance with the experiment. This work was supported by grants from the Key Research and Development Program of Science and Technology Department of Henan Province [No. 241111312300 to W.Z.]. We also express gratitude to the Home for Researchers editorial team (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.home-for-researchers.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.home-for-researchers.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for language editing service.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eRNA sequencing data and analysis code are available from W.Z. or Z.Z. on reasonable request. CHPF is available online at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/yihan1221/CHPF\u003c/span\u003e\u003cspan address=\"https://github.com/yihan1221/CHPF\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Publicly available datasets were analyzed in this study: datasets stored in TCGA for cholangiocarcinoma at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"http://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and GSE107943 at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. All other datasets used and/or analyzed during the current study are available within the manuscript and its supplementary information file.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBlechacz B, Komuta M, Roskams T, Gores GJ. Clinical diagnosis and staging of cholangiocarcinoma. 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Nat Commun. 2021;12(1):1088.\u003c/span\u003e\u003c/li\u003e\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":"cell-death-and-disease","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddis","sideBox":"Learn more about [Cell Death \u0026 Disease](http://www.nature.com/cddis/)","snPcode":"41419","submissionUrl":"https://mts-cddis.nature.com/cgi-bin/main.plex","title":"Cell Death \u0026 Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"distal cholangiocarcinoma, dedifferentiated Schwann cells, perineural invasion, Lactate, HMGB1","lastPublishedDoi":"10.21203/rs.3.rs-5240585/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5240585/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDistal cholangiocarcinoma (dCCA) is a highly lethal malignancy that accounts for approximately 40% of patients with primary cholangiocarcinoma. Remarkable cellular heterogeneity and perineural invasion (PNI) are two typical features of dCCA. Deciphering the complex interplay between neoplastic and neural cells is crucial for understanding the mechanisms propelling PNI-positive dCCA progression. Herein, we conduct single-cell RNA sequencing on 24 715 cells from two pairs of PNI-positive dCCA tumors and adjacent tissues, identifying eight unique cell types. Malignant cells exhibit significant inter- and intra-tumor heterogeneity. We delineate the compositional and functional phenotypes of five Schwann cell (SC) subsets in PNI-positive dCCA. Moreover, our analyses reveal two potential cell subtypes critical to forming PNI: \u003cem\u003eNEAT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e malignant cells characterized by hypoxia and \u003cem\u003eGFAP\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e dedifferentiated SCs featuring hypermetabolism. Further bioinformatics uncover extensive cellular interactions between these two subpopulations. Functional experiments confirm that lactate in the hypoxic tumor microenvironment can induce \u003cem\u003eGFAP\u003c/em\u003e-dedifferentiation in SCs, which promotes cancer cell invasion and progression through upregulating \u003cem\u003eHMGB1\u003c/em\u003e. Taken together, our findings offer a thorough characterization of the transcriptional profile in PNI-positive dCCA and unveil potential therapeutic targets of dCCA PNI.\u003c/p\u003e","manuscriptTitle":"Single-cell analysis reveals that GFAP+ dedifferentiated Schwann cells promote tumor progress in PNI-positive distal cholangiocarcinoma via lactate/HMGB1 axis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-10 16:15:50","doi":"10.21203/rs.3.rs-5240585/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-12-06T11:30:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-11-30T12:03:08+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-11-18T21:35:09+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-11-13T22:52:41+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-11-12T14:47:02+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-11-09T07:34:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-11T10:14:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-10T14:44:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cell Death \u0026 Disease","date":"2024-10-10T14:44:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cell-death-and-disease","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddis","sideBox":"Learn more about [Cell Death \u0026 Disease](http://www.nature.com/cddis/)","snPcode":"41419","submissionUrl":"https://mts-cddis.nature.com/cgi-bin/main.plex","title":"Cell Death \u0026 Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16718583-aef4-49f5-8cba-422795c26856","owner":[],"postedDate":"December 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40033108,"name":"Biological sciences/Cancer/Cancer microenvironment"},{"id":40033109,"name":"Biological sciences/Cell biology/Mechanisms of disease"},{"id":40033110,"name":"Biological sciences/Cancer/Tumour heterogeneity"}],"tags":[],"updatedAt":"2025-03-28T07:07:51+00:00","versionOfRecord":{"articleIdentity":"rs-5240585","link":"https://doi.org/10.1038/s41419-025-07543-x","journal":{"identity":"cell-death-and-disease","isVorOnly":false,"title":"Cell Death \u0026 Disease"},"publishedOn":"2025-03-27 04:00:00","publishedOnDateReadable":"March 27th, 2025"},"versionCreatedAt":"2024-12-10 16:15:50","video":"","vorDoi":"10.1038/s41419-025-07543-x","vorDoiUrl":"https://doi.org/10.1038/s41419-025-07543-x","workflowStages":[]},"version":"v1","identity":"rs-5240585","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5240585","identity":"rs-5240585","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}