ABSTRACT Pancreatic cancer (PC) is one of the most common malignant tumors of the digestive tract and has a very high mortality rate worldwide. Different PC patients may respond differently

to therapy and develop therapeutic resistance due to the complexity and variety of the tumor microenvironment. The Eph/ephrin signaling pathway is extensively involved in tumor-related

biological functions. However, the key function of the Eph/ephrin signaling pathway in PC has not been fully elucidated. We first explored a pan-cancer overview of Eph/ephrin signaling

pathway genes (EPGs). Then we grouped the PC patients into 3 subgroups based on EPG expression levels. Significantly different prognoses and tumor immune microenvironments between different

signature and validated it in internal and external test sets. The receiver operating characteristic (ROC) curves of our model exhibited great predictive performance. Meanwhile, we further

validated the results through qRT-PCR and immunohistochemistry. Overall, this research provides fresh clues on the prognosis and therapy of PC as well as the theoretical groundwork for

future Eph/ephrin signaling pathway research. SIMILAR CONTENT BEING VIEWED BY OTHERS EPYC FUNCTIONS AS A NOVEL PROGNOSTIC BIOMARKER FOR PANCREATIC CANCER Article Open access 06 January 2024

MACHINE LEARNING IDENTIFIES SLC6A14 AS A NOVEL BIOMARKER PROMOTING THE PROLIFERATION AND METASTASIS OF PANCREATIC CANCER VIA WNT/Β-CATENIN SIGNALING Article Open access 24 January 2024

INTEGRATIVE MULTI-OMICS ANALYSIS REVEALS THE ROLE OF TOLL-LIKE RECEPTOR SIGNALING IN PANCREATIC CANCER Article Open access 02 January 2025 INTRODUCTION Pancreatic cancer (PC) is a common

malignant tumor of the digestive tract characterized by aggressiveness and poor prognosis1. Numerous studies have largely established that the mechanisms driving its onset and progression

are primarily genetic in nature. Despite continued advances in early detection and therapy, patients have a 5-year survival rate of approximately 12%2. The high mortality rate of PC is

mainly due to the lack of biomarkers for early detection and the poor efficacy of chemotherapy. Chemotherapy and surgical excision are the main treatments for PC. Nevertheless, most

individuals have metastases by the time of diagnosis. Recently, advanced PC has been treated clinically with molecularly targeted drug therapy. And probably due to tumor heterogeneity, no

single molecularly targeted therapy has achieved consistent success3. Because of this, it is very important to find new biomarkers to predict the outcome of PC patients and give them

personalized treatment. Eph/ephrin signaling pathway is essential to tumor biology and is the focus of studies on cancer targeted therapy. Eph receptor is the most common subfamily of

receptor tyrosine kinases, which triggers signal propagation through interactions with ephrin ligands that are also present on the cell surface4,5. Synaptic plasticity, neuron regeneration,

cancer progression, immune system, lymphangiogenesis, angiogenesis, and some key physiologic functions of the pancreas are all influenced by Eph receptors and ephrins in adults6,7. Abnormal

expression of Eph and ephrin has been found in several types of cancer in recent years, like neuroblastoma8, breast carcinomas9,10,11, lung cancer12, gastric cancer13,14, prostate cancer15,

ovarian cancer16, colon cancer17, PC7,18, and melanoma19. Eph receptors are becoming recognized as master modulators capable of enhancing or suppressing the activity of the cancer signaling

network, and Eph family members have been recognized as potential anticancer targets. Meanwhile, Eph and ephrin expression have the potential to be a strong predictor of prognosis and

treatment sensitivity. For instance, elevated EPHA2 expression could promote susceptibility to dasatinib and resistance to trastuzumab20,21,22. Therefore, the development of PC risk

stratification tools based on the Eph/ephrin signaling pathway genes (EPGs) is promising. We first analyzed the genomic variations and expression profiles of the EPGs across different cancer

types to explore the characterization of the Eph/ephrin signaling pathway in tumors. To explore EPG-related molecular subtypes in PC patients, we categorized PC samples into different

subtypes according to the expression level of EPG and investigated their association with prognosis, immune microenvironment, and drug sensitivity. To further identify the genes that

characterize the different subtypes, we performed differential expression analysis among the PC subtypes. Differentially expressed genes (DEGs) were then filtered by tumor and normal tissue

difference analysis and prognostic analysis. Finally, a novel signature including 6 genes was constructed using LASSO-Cox regression and expression status of model genes were then tested by

qRT-PCR and immunohistochemistry. Overall, this research provides fresh clues on the prognosis and therapy of PC as well as the theoretical groundwork for future Eph/ephrin signaling pathway

research. METHODS DATA COLLECTION AND PROCESSING The TCGA, GEO, ICGC, ArrayExpress, and GTEx databases were utilized to collect gene expression data and clinical annotations for the

datasets. Individuals that lacked survival information were not included in the analysis. Batch effects were corrected by the “SVA (version 3.42.0)” package23. We combined the GSE5749524,

GSE2873525,26, GSE6245227, E-MTAB-613428, and TCGA-PAAD (https://portal.gdc.cancer.gov/) sets, which included 635 PC individuals, and randomly divided into a training set (319 samples) and

an internal validation set (316 samples) for future research. Meanwhile, we combined the ICGC-CA and ICGC-AU for an external test, with 295 samples serving as the external validation set29.

The Molecular Signatures Database was used to obtain the EPGs30, 37 EPGs were found in 930 PC individuals were included in this analysis. R (version 4.13) and “Bioconductor (version 3.14)”

package were used for this analysis31. _PAN_-_CANCER_ ANALYSIS The variations of EPGs in different cancers are not well summarized. Copy number variant (CNV), single nucleotide variation

(SNV), and DNA methylation data taken from the TCGA were analyzed and visualized for a pan-cancer analysis of variants of EPGs. Additionally, differential mRNA expression (FoldChange > 1,

FDR < 0.05) and univariate Cox regression analysis were applied to study the expression status and prognostic values of EPGs in multiple human tumors using “limma” and “survival”

packages32. CLUSTER ANALYSIS ACCORDING TO EPH/EPHRIN SCORES Eph/ephrin pathway enrichment scores were calculated by ssGSEA. We calculated high or low expression thresholds for each gene in

PC samples to determine their expression states. Subsequently, the obtained expression state matrix was used for clustering and visualization. Both rows and columns were clustered using the

Ward.D method, and the samples were categorized into three subtypes with reference to previous literature33,34,35. The “kruskal.test” function was then conducted to test the differences in

Eph/ephrin scores among the three clusters. We utilized the “ggpubr” package to show the score levels of different clusters. According to the Eph/Ephrin scores of the three clusters, we

named them as Eph/ephrin-inactive cluster (C3), Eph/ephrin-normal cluster (C1), and Eph/ephrin-active cluster (C2). Finally, KM survival curves were generated using the “survminer (version

0.4.9)” package to compare the prognostic differences among the three clusters by log rank test. DRUG SENSITIVITY ANALYSIS “OncoPredict (version 0.2)” is the package that predicts in vivo

medication responses and biomarkers in cancer patients using data from cell line screening. By combining the GDSC database and the “oncoPredict (version 0.2)” package, we assessed the

variations in common chemotherapeutic and targeted drug sensitivity among various subtypes36. ASSOCIATIONS OF THE EPH/EPHRIN SCORES WITH THE IMMUNE CELL INFILTRATION (ICI) The set of 29

immune-related gene sets were evaluated using ssGSEA. The R programs “ggplot2” and “dplyr” were used to create a heatmap showing the link between EPGs and ICI, with statistical analysis

using Spearman's coefficient. In addition to EPGs, the relation between the Eph/ephrin signaling pathway scores and immune cells was determined based on the results of ssGSEA. Finally,

scatter plots were constructed by “ggscatterstats” function of the “ggstatsplot (version 0.11.1)” package to reveal the correlation between the most relevant classical immune cell

populations and Eph/ephrin signaling pathway scores. DISCREPANCY IN ICI AND IMMUNE CHECKPOINT GENES (ICGS) EXPRESSION ACROSS DIFFERENT SUBTYPES Using transcriptome data, the “ESTIMATE”

package was utilized to assess the difference in immune characteristics among three PC clusters37. The infiltration abundance of each immune cell type was studied in depth using various

immunocorrelation algorithms (including TIMER、QUANTISEQ、MCPCOUNTER、XCELL、EPIC, CIBERSORT). The “kruskal.test” in R was utilized to compare the differences in ICI abundance and expression

levels of common ICGs between the different subtypes, showing only statistically significant findings (p < 0.05). CONSTRUCTION OF THE PROGNOSTIC MODEL We used the “limma” package to find

DEGs among three PC subtypes (FDR < 0.05). Then 160 DEGs that were also differentially expressed between tumor and normal tissues for subsequent analysis (|log2(FC)|> 1, FDR < 0.05)

were included in the follow-up analysis. Later, these genes underwent univariate Cox regression analysis with a filtering criterion of p < 0.05. The LASSO regression analysis was

conducted to address overfitting by “glmnet” package38. Given that Cox regression does not require assumptions about the shape of the baseline risk function, we further create signatures

through multivariate Cox regression analysis and calculate risk scores by “predict” R function. To assess the predictive power of the model and to ensure that the high- and low-risk groups

were numerically balanced, individuals were categorized into high- and low-risk groups based on the median risk score of the training cohort. The following analyses were performed on all

cohorts for the internal and external tests of our model: (1) to visualize sample classification by t-distributed stochastic neighbor embedding (t-SNE) and principal component analysis (PCA)

analysis; (2) to create a heatmap of gene expression using R's “pheatmap”; (3) KM approach was utilized to perform a survival analysis; (4) receiver operating characteristic (ROC)

curves were created for evaluating the predictive performance of our signature. THE DISCREPANCY OF DRUG SENSITIVITY, ICI, AND ICG EXPRESSION BETWEEN DIFFERENT RISK SUBGROUPS Each PC

patient's drug sensitivity was predicted using the R “OncoPredict (version 0.2)” package. Only agents that achieved statistical significance across all training and test groups were

regarded as authentic and credible targeted agents. In addition, we compared the differences in ICI and ICGs in high- and low-risk groups by similar methods as above. ISOLATION OF TOTAL RNA

WITH QRT-PCR AND IMMUNOHISTOCHEMISTRY The American Type Culture Collection supplied the HPDE6-C7 normal human pancreatic ductal cell line. Bxpc-3 and CFPAC-1 are two human PC cell lines that

were supplied by our research laboratory. The HPDE6-C7 and BxPC-3 lines' cells were cultured in DMEM supplemented with 10% FBS. IMDM supplemented with 10% FBS was used to culture

CFPAC-1 cells. TRIzol (Accurate Biotechnology) was used to extract total RNA from human PC cell lines. Reverse transcription was then performed on the RNA to produce cDNA using the Evo M-MLV

RT Kit. The SYBR Green Premix Pro Taq HS qPCR Kit was used to quantify the expression levels of 6 genes. The RNA level expression was analyzed and quantified using the ΔΔCt method, and

differences in the expression of target genes in normal and pancreatic cancer cell lines were compared by t-test. The primer sequences employed were as follows, which were obtained from Gene

Pharma (Suzhou) Co., Ltd.: for human KRT17, 5′-AGGTGAAGATCCGTGACTGG-3′ (Forward), 5′-TCTTGTTCTGCAGCTCCTCA-3′ (Reverse); for human DSG2, 5′-ACGTAGAAGTTACGCGCATAAA-3′ (Forward),

5′-GGGTCACAATTCCTTCGTTAGTT-3′ (Reverse); for human LOXL2, 5′-CCATCCTCTCAACCTACCGC-3′ (Forward), 5′-GTCTTGCCCTCCTTCACCTC-3′ (Reverse); for human DCBLD2, 5′-AGCAAGGTGATGGATGTGGA-3′ (Forward),

5′-TGCGAACTCTCTCTCCCATC-3′ (Reverse); for human DKK1, 5′-ATAGCACCTTGGATGGGTATTCC-3′ (Forward), 5′-CTGATGACCGGAGACAAACAG-3′ (Reverse); for human LGR5, 5′-CACTTATGACCTGCCTCCCA-3′ (Forward),

5′-ACAGAGGAAAGATGGCAGCT-3′ (Reverse); for human GAPDH, 5′-GGTCTCCTCTGACTTCAACA-3′ (Forward), 5′-GTGAGGGTCTCTCTCTTCCT-3′ (Reverse). Tissue samples from 10 PC patients who underwent surgery

from January 2023 to September 2023 were collected from our hospital. The formalin-fixed paraffin-embedded PC tissues and paired paracancerous tissues of ten patients were subjected to

immunohistochemical analysis. Primary antibodies were incubated with the tissue slices for a whole night at 4 °C, including DCBLD2 (PTG, 13168), DKK1 (PTG, 21112), DSG2 (PTG, 21880), KRT17

(Servicebio, GB12363), and LOXL2 (Servicebio, GB11849). Subsequently, the slices were treated for fifty minutes at room temperature with the matching secondary antibody. Lastly,

representative pictures were collected using an Olympus scanning microscope, and the mean optical density (MOD) values of the cancer and controls were compared by t-tests. All methods were

performed in accordance with the relevant guidelines and regulations. CONSENT TO PARTICIPATE AND ETHICS APPROVAL The Ethics Committee of Dalian Medical University's First Affiliated

Hospital examined and authorized the investigations involving human subjects (ID: PJ-KS-KY-2023-482). Written informed consent was given by the participants to take part in this research.

RESULTS _PAN_-_CANCER_ LANDSCAPE OF THE EPGS The current research is illustrated in the flow chart (Fig. 1). The function of the Eph/ephrin pathway in tumor development has not been

clarified, and EPGs' whole-cancer characteristics are not well-summarized. Therefore, thorough research of EPGs in a variety of tumors from the perspectives of CNV, SNV, mRNA expression

features, prognostic performance, and DNA methylation is necessary. WASL, MMP9, EPHB3, EPHB4, LIMK1, EPHA1, ACTB, and other genes exhibit an extensive expansion of CNV in multiple cancers.

EPHA2, TIAMI, AP2A2, and EPHA7 exhibited extensive defects (Fig. 2A). Moreover, most EPGs in UCEC have a greater degree of SNV (Fig. 2B). We also discovered that all EPGs were expressed

differently in at least one cancer type (Fig. 2C). Then we developed a survival landscape of EPGs according to TCGA's connection between patient survival and gene expression. It can be

found that most of the EPGs act as protective genes in LUSC and KIRC and as risky genes in PC, ACC, LIHC, KICH, and other types of tumors (Fig. 2D). Considering that aberrant DNA methylation

in promoters is also closely related to tumorigenesis39, we analyzed DNA methylation of EPGs in different tumors. We observed that EPGs showed mostly hypomethylation in 20 cancer types

(Fig. 2E). CLASSIFICATION OF PC INTO THREE SUBTYPES BASED ON EPH/EPHRIN SCORES To further analyze the correlation between EPGs and PC, 930 PC individuals were split into three groups

according to the EPGs' expression levels (Fig. 3A). The violin plot displayed the enrichment scores for 3 groups (C2 > C1 > C3, _p_ < 2.2e − 16) (Fig. 3B). In addition, KM

analysis on three clusters showed significantly varied PC survival rates (Fig. 3C). Cluster 3 had a much better prognosis than clusters 1 and 2, which is identical to the situation

represented in Fig. 2D. Most EPGs are risky in PC, and the patients with low Eph/ephrin scores had longer survival. DIFFERENCES IN DRUG SENSITIVITY IN DIFFERENT SUBTYPES OF PC PATIENTS

Considering that chemotherapy and molecularly targeted therapies are now commonly used to treat PC, we assessed the drug sensitivity of these three molecular subtypes with the help of the

“OncoPredict” package. The results demonstrated the following differences in drug sensitivity among Eph/ephrin clusters: 5-Fluorouracil: C2 > C3 (_p_ = 1.9e − 06); cisplatin: C1 > C2

(_p_ = 4.2e − 08); gemcitabine: C1 > C2 (_p_ = 0.0069); irinotecan: C3 > C2 (_p_ = 1e − 09); oxaliplatin: C3 > C2 (_p_ = 2.1e − 07); paclitaxel: C2 > C3 (_p_ = 7.1e − 05);

camptothecin: C3 > C2 (_p_ = 1.3e − 05); dasatinib: C1 > C3 (_p_ = 8.5e − 14); sorafenib: C3 > C2 (_p_ = 9.6e − 11) (Fig. 4). ASSOCIATIONS OF THE EPH/EPHRIN SCORES WITH THE IMMUNE

CELLS The prognosis of cancer is associated with immune cells, which are key components of the tumor microenvironment (TME) and effective anticancer treatment targets40. The Eph/ephrin

pathway is also important in controlling the tumor immune microenvironment41. To learn more about how the Eph/ephrin pathway and immunity are related, we conducted a relevance analysis

between the EPGs and ICI in PC patients (Fig. 5A). Most EPGs were found to be associated with ICI. Among them, genes with significant positive correlation included TIAM1, SDC2, MMP9, ARPC5,

ACTR3, LIMK1, and those with significant negative correlation included EFNA2, EFNA3, EFNA5, EPHA1, EPHB4. Next, the relationship between Eph/ephrin score and ICI was analyzed (Fig. 5B). It

was found to have a positive relationship with macrophage infiltration, parainflammation, and APC co-stimulation, but a negative relationship with Tfh cells (Fig. 5C–F). DISCREPANCY IN ICI

AND ICGS EXPRESSION ACROSS THREE SUBTYPES OF PC PATIENTS We then compared differences in the immune microenvironment among three subtypes. The findings demonstrated that C3 had lower levels

of immuneScore, stromalScore, and estimateScore than C1 and C2 (Kruskal–Wallis test, _p_ < 2.2e − 16) (Fig. 6A). Meanwhile, we found that macrophages, cancer associated fibroblast, and

myeloid dendritic cells show a lower expression in the C3 subgroup, while C1 exhibited higher levels (Fig. 6B). In addition, we also found the ICGs expression was significantly increased in

the C1 (Fig. 6C). ESTABLISHMENT OF THE PROGNOSTIC MODEL CONSISTING OF SIX GENES We first analyzed the DEGs of the three PC subtypes, and a total of 8449 DEGs were obtained. Then we obtained

160 genes from them that were differentially expressed not only between the three PC subtypes but also between PC and normal individuals in the three datasets (Fig. S1A). Subsequently, the

DEGs were subjected to univariate Cox regression analysis to obtain 80 genes with prognostic value for subsequent analysis. Then we employed LASSO regression to create a prediction signature

to predict the prognosis of PC individuals. Eight genes were selected from 80 genes by the LASSO regression (Fig. S1B,C). Finally, six genes were preserved for conducting multivariate Cox

regression analysis (Fig. S1D). All PC individuals were further split into low- and high-risk subgroups according the median risk score. Samples with higher risk score had a higher chance of

dying (Fig. 7A,B). Then, T-SNE and PCA results illustrated that low- and high-risk individuals could be effectively distinguished (Fig. 7C,D). The heatmap showed that the remaining five

genes except LGR5 were more highly expressed in the high-risk group (Fig. 7E). The high-risk group had shorter overall survival (Fig. 7F). Meanwhile, the AUC of ROC curves for 1-year,

2-year, and 3-year survival were 0.713, 0.718, and 0.7 in the train cohort (Fig. 7G). INTERNAL AND EXTERNAL VERIFICATION OF OUR MODEL To assess the signature's reliability and validity

in the internal and external validation sets, risk scores were created and individuals were split into high- and low-risk subgroups based on the median risk scores of the training cohort

(Figs. S2A and S3A). The distributions of survival status and risk scores were similar to the training set (Figs. S2B and S3B). Through t-SNE and PCA, it has been shown that two categories

can be differentiated (Figs. S2C,D and S3C,D). Heatmaps revealed the presence of highly expressed genes (KRT17, DSG2, LOXL2, DCBLD2, and DKK1) and genes with low expression (LGR5) in the

high-risk subgroup in both internal and external verification sets (Figs. S2E and S3E). In addition, high-risk individuals have a poorer prognosis in test sets (Figs. S2F and S3F). The ROC

curves indicate that our signature's diagnostic value is satisfied (Figs. S2G and S3G). In addition, Sankey diagram showed the relationship between molecular subtypes and risk subgroups

(Fig. S4A). It can be found that the number of high- and low-risk individuals in C1 and C2 is similar, while C3 is mainly composed of low-risk patients. To further explore the prognostic

value of the prognostic model in different EPH-related subtypes, we compared the prognostic differences between samples from high and low risk subgroups in different subtypes. Due to the

small sample of C3 subtype and the majority of low-risk patients, only C1 and C2 were analyzed, and it could be found that the prognosis of high-risk patients was equally worse than that of

the low-risk group in C1 and C2 (Fig. S4B,C). DIFFERENCES IN DRUG SENSITIVITY, ICI, AND ICG EXPRESSION BETWEEN INDIVIDUALS IN HIGH- AND LOW-RISK SUBGROUPS Similarly, we compared whether

there were differences in the drug sensitivity of individuals in the high- and low-risk subgroups to the nine drugs mentioned above. The results revealed that low-risk individuals might

benefit from camptothecin, cisplatin, gemcitabine, irinotecan, oxaliplatin, and sorafenib; however, high-risk populations might benefit from dasatinib in all sets (Fig. S5). Several

immune-related algorithms were used to investigate immune responses, which were displayed in heatmaps (Fig. 8A–C). After combining all of the variations in training and test cohorts between

the two groups, we discovered that several anti-tumor immune cells had lower proportions in the high-risk subgroup, including B cells and CD4+ T cells. In addition, M1 macrophage expression

was upregulated in the high-risk group. Because ICGs are so important in immunotherapy, we then studied the differences in ICG expression between the two subgroups. CD70, TNFSF4, CD80, CD274

and PDCD1LG2 were upregulated in the high-risk subgroup, while JAK2 and CD160 were reversed (Fig. 8D–F). VERIFICATION OF SIX SIGNATURE GENES BY QRT-PCR AND IMMUNOHISTOCHEMISTRY We found

that six genes in our model were significantly upregulated in PC, according to the outcomes of the differential expression analysis (Fig. S6). We then used qRT-PCR to validate the expression

patterns of these model genes in normal pancreatic cell lines and PC cell lines. We found the mRNA levels of DCBLD2, DKK1, DSG2, KRT17, and LGR5 were higher in at least one of the PC cell

lines, while the opposite was true for LOXL2 (Fig. 9A). Considering that LGR5 has been widely demonstrated to stain significantly higher in PC tissues than in normal pancreatic tissues42,43,

we performed immunohistochemical analyses of only the remaining five target genes. The levels of DCBLD2, DKK1, DSG2, KRT17, and LOXL2 proteins were determined by immunohistochemistry, and

the MOD values were assessed. Figure 9B shows representative images of DCBLD2, DKK1, DSG2, KRT17, and LOXL2 protein levels, respectively. Similarly, the MOD values of DCBLD2, DKK1, DSG2,

KRT17, and LOXL2 were significantly higher in PC tissues compared with the controls (Fig. 9B). DISCUSSION PC is a type of gastrointestinal cancer that has a high fatality rate1. Over the

last few years, improved treatments have improved the treatment and prognosis of PC patients, but the survival rate remains low. The Eph/ephrin signaling pathway is extensively involved in

tumor-related biological functions. Intracellular signaling is triggered in a bidirectional way after ligand–receptor contact. Eph receptors and ephrin ligands are well-known for their roles

in carcinogenesis, cancer development, and neovascularization in a variety of cancers. In addition, researches have shown that they could be potential novel anticancer targets44. However,

there was a lack of a comprehensive study of the Eph/ephrin signaling pathway in PC. In our study, we started by looking into genetic mutations in EPGs in different cancers. Then, we

analyzed the changes in Eph/ephrin pathway expression and determined whether these genes were present as protective or risk genes in various forms of tumor. Results showed that the majority

of EPGs were found to be risk genes in various cancers. Then we investigated the relationship between EPGs and PC, and classified 930 samples into 3 clusters according to the expression

levels of EPGs. We found that C2 with high Eph/ephrin scores had a significantly worse prognosis than C1 and C3, which again suggests that the Eph/ephrin pathway may play an oncogenic role

in PC. Adjuvant chemotherapy after surgical resection is usually the preferred treatment option for early stage PC, and its therapeutic efficacy has been widely recognized45. However, the

complexity and heterogeneity of TME in PC always leads to the development of therapeutic resistance and differential response to therapy in different patients. As a result, we confirm the

sensitivity of several of the most commonly used medications in PC therapy. We found that the different clusters had distinct medication sensitivities, implying that patients could receive a

more tailored treatment plan based on their Eph/ephrin-related gene expression patterns. The immune cells within the TME perform a vital function in tumorigenesis, so we investigated the

relationship between Eph/ephrin pathway score and ICI. We discovered that Eph/ephrin pathway score was positively correlated with macrophage infiltration, parainflammation, and APC

co-stimulation, but negatively correlated with Tfh cells. TME is an intricate assembly of the tumor, immune, stromal, and extracellular components46. We found that C1 with a poorer prognosis

may have a higher immune abundance. Furthermore, we found that ICGs are more highly expressed in the C1. The C1 cluster's strong immune cell infiltration may be a local compensatory

phenomenon of active ICGs. Despite the high level of anticancer immunity in C1, high expression of ICG in C1 may inhibit immune cell function and prevent the body from generating an

effective antitumor immune response, which could lead to tumor immune escape and a poor prognosis. Molecular typing aims to classify tumors into different subtypes based on their molecular

characteristics, helping to understand tumor heterogeneity and providing a basis for personalized treatment. However, molecular typing still has some shortcomings; it cannot accurately

predict the prognosis of a specific patient, whereas prognostic models can be used as tumor-specific biomarkers to predict a patient's risk score and prognosis. Thus, we developed a

prognostic signature comprised of 6 genes (i.e., LOXL2, DCBLD2, DKK1, DSG2, LGR5, and KRT17). LOXL2 has been shown to enhance tumor progression, invasion, and metastasis through both

cell-autonomous and non-cell-autonomous mechanisms in many tumor entities47. DCBLD2 is intimately linked to tumor invasion and migration and is overexpressed in lung, colorectal, and

glioblastoma malignancies48,49,50. A member of the DKK family, DKK1 is linked to aggressive characteristics and a worse prognosis in patients with PC51. It has been shown that DKK1 is

associated with the accumulation of bone marrow-derived suppressor cells in PC and contributes to the suppression of anti-tumor T cell responses52. The dysregulation of DSG2 is thought to be

associated with poor prognosis in various tumors53,54,55,56. In addition, it has been found that DSG2 deficiency promotes the tumorigenic behavior of PC cells57. LGR5 is considered as a

marker for adult stem cells58. It is associated with tumorigenesis and metastasis and is a marker of predictive response to platinum-based drug treatment59. KRT17 belongs to the type I

keratin family and is involved in the development of or associated with bad prognosis in various tumors60,61,62,63,64,65. With the use of our signature, PC patients in all cohorts may be

successfully divided into two subgroups, with the high-risk group having a worse prognosis than the low-risk group. Our signature has a high predictive performance, as indicated by the ROC

curves. Following a thorough investigation of the quantity of immune cell infiltration, it was shown that the low-risk group had higher levels of various anti-tumor immune cells, such as B

cells66, and T cells CD4+67. Because ICGs are so important in immunotherapy, we then studied the differences in ICGs expression between two subgroups. After combining differences in all

sets, we found that CD70, TNFSF4, CD80, CD274, and PDCD1LG2 were upregulated in the high-risk subgroup, while CD40LG, JAK2, and CD160 were reversed. It is well known that cancer cells are

capable of activating immunological checkpoint pathways with immunosuppressive properties68. Thus, these ICGs are expected to act as potential efficient therapeutic targets. Finally, we

verified the expression of the model genes by qRT-PCR and immunohistochemistry. The experimental validation results were generally consistent with the results of our bioinformatics analysis.

However, LOXL2 was downregulated in qRT-PCR results but upregulated in immunohistochemistry. We believe that this discrepancy may be caused by a variety of factors, including tumor

heterogeneity, differences in the tumor microenvironment, epigenetic modifications, cell culture conditions, and differences in post-transcriptional regulatory mechanisms. The combined

effect of these factors may lead to significant differences in gene expression patterns between cell lines cultured in vitro and in situ tumor tissues. This is the first report to synthesize

the role of Eph/ephrin signaling pathway in PC using bioinformatics and experimental validation. The shortcoming is that although we elucidated the relationship between Eph/ephrin signaling

and PC prognosis, biological behaviors, tumor immune microenvironment, and drug sensitivity by constructing Eph/ephrin signaling-associated molecular subtypes and prognostic models, the

dataset of the present study was retrospective and was obtained from publicly available databases. Therefore, this research should be validated in prospective clinical studies with large

samples. Also because of the small sample size in the C3 subtype and the majority of the sample being low-risk patients, we only supplemented the analysis of the survival performance of the

prognostic model in the C1 and C2 subtypes. Meanwhile, the lack of theoretical basis for dividing the sample according to the median risk score is based only on previous similar studies, and

patients with very similar risk scores may be categorized differently depending on whether their values are slightly greater or less than the median, so further exploration is still needed

for the cutoff values for high and low risk. Finally, more mechanistic experiments are needed to further investigate the role of Eph/ephrin signaling in the occurrence and development of PC.

CONCLUSION Our research identified six genes (i.e., LOXL2, DCBLD2, DKK1, DSG2, LGR5, and KRT17) with the potential to serve as a novel biomarker of the progression and prognosis of PC by

integrating a series of bioinformatics techniques. Based on Eph/ephrin scores, PC patients could be divided into 3 subgroups (Eph/ephrin-inactive, Eph/ephrin-normal, and Eph/ephrin-active)

with distinct prognosis, immune characteristics, and drug sensitivity. Our study shed fresh light on the prognosis and therapy of PC, as well as provided the theoretical groundwork for

future Eph/ephrin signaling pathway research. DATA AVAILABILITY The datasets analysed during the current study are available in the TCGA (https://portal.gdc.cancer.gov/), GEO

(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57495; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28735; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62452), ICGC

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Scholar  Download references AUTHOR INFORMATION Author notes * These authors contributed equally: Jifeng Liu, Qihang Yuan and Xu Chen. AUTHORS AND AFFILIATIONS * Department of General

Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China Jifeng Liu, Qihang Yuan, Xu Chen, Yao Yang, Tong Xie, Bing Qi, Shuang Li & Dong Shang *

Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China Jifeng Liu, Qihang Yuan, Xu Chen, Yunshu Zhang, Bing Qi, Shuang Li & 

Dong Shang * Institute of Integrative Medicine, Dalian Medical University, Dalian, Liaoning, China Yao Yang, Tong Xie, Yunshu Zhang & Dong Shang Authors * Jifeng Liu View author

publications You can also search for this author inPubMed Google Scholar * Qihang Yuan View author publications You can also search for this author inPubMed Google Scholar * Xu Chen View

author publications You can also search for this author inPubMed Google Scholar * Yao Yang View author publications You can also search for this author inPubMed Google Scholar * Tong Xie

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* Dong Shang View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS This study's design, analysis, and paper preparation benefited from the

efforts of Jifeng Liu, Qihang Yuan, and Xu Chen. Yao Yang and Yunshu Zhang were responsible for the collection of clinical samples and experimental analysis. Tong Xie contributed to the

revision of the manuscript. Shuang Li and Bing Qi were involved in the design and data analysis. Prof. Dong Shang proposed the study conceptualization, reviewed the manuscript. CORRESPONDING

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