Identification of the anoikis-related prognostic gene signature in pancreatic adenocarcinoma

Identification of the anoikis-related prognostic gene signature in pancreatic adenocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of the anoikis-related prognostic gene signature in pancreatic adenocarcinoma Wei Chen, Zeyan Xu, Jingjing Jiang, Hong Chen, Ruihua Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2326682/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Pancreatic adenocarcinoma (PAAD) remains a tumor with high malignancy and poor prognosis. Anoikis can regulate tumor cell invasion and metastasis, which in turn leads to poor prognosis for cancer patients. However, the role of anoikis-related genes (ARGs) in PAAD remains unclear. In our study, we performed a comprehensive bioinformatics analysis to construct a prognostic gene model. Correlations between ARGs and tumor immune infiltration, tumor mutation burden and microsatellite instability were assessed using Spearman correlation analysis. A total of 23 ARGs were upregulated in PAAD. Functional enrichment analysis revealed that these 23 ARGs were mainly involved in proteoglycans in cancer, regulation of anoikis, lipid and atherosclerosis, focal adhesion, and regulation of apoptotic Prognostic analysis showed that patients with PAAD with high expression of PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3 had a low survival rate. The prognostic ARGs model constructed using the above six prognostic genes was constructed to predict the overall survival of PAAD patients. There was a significant correlation between prognostic ARGs and immune cell infiltration and tumor mutation burden, while there was no correlation with microsatellite instability. Meanwhile, knockdown of BCL2L1 expression could inhibit the proliferation of pancreatic cancer cells. In conclusion, we performed a comprehensive bioinformatics analysis to identify PAAD patients containing six genes (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3). Further studies are needed to validate our findings. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introducion Pancreatic adenocarcinoma (PAAD) is a common malignant tumor with a very high degree of malignancy(Klein, 2021 ). The main treatment methods currently available are surgery, chemotherapy and radiation therapy(Torphy, Fujiwara, & Schulick, 2020 ). Early surgical resection is currently the only effective treatment. However, because early detection and diagnosis of pancreatic cancer is very difficult, surgical opportunities are often missed and lead to poor prognosis(Cai et al., 2021 ). Therefore, it becomes extremely important to reveal the genetic profile of pancreatic cancer prognosis and to develop new approaches for the treatment of pancreatic cancer. Anoikis, a programmed cell death, is induced when cells are detached from the extracellular matrix and is a key mechanism to prevent the growth and attachment of apposition-independent cells to inappropriate matrices, thereby avoiding distal organ colonization. Cancer cells resist anoikis through two major pathways, anchorage-independent growth and epithelial-mesenchymal transition, thereby promoting cancer progression and metastasis.(Paoli, Giannoni, & Chiarugi, 2013 ) Cancer cells must overcome anoikis (detachment-induced death) to successfully metastasize. It has been found that high expression of IL1RAP in Pancreatic can inhibit anoikis to promote cancer progression(Zhang et al., 2021 ). Therefore, it is important to further explore the mechanism of the role of anoikis in the development of pancreatic cancer. In this study, we used bioinformatics analysis to investigate ARGs expression of proprotein and its prognostic significance as well as immune infiltration. Our data may provide additional evidence for prognostic biomarkers and therapeutic targets for pancreatic cancer. Methods Data Collection RNA-seq transcriptomic data and corresponding clinical medical information of pancreatic cancer patients were obtained from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ). A total of 179 Pancreatic tumor samples and 4 adjacent normal samples were included in our study, and supplemented with RNA transcriptome data from the GTEx database of 328 healthy human normal tissue samples. ARGs were obtained from the GeneCards database ( https://www.genecards.org/ ). The "limma" and "remodeling2" software packages were used to identify differences in ARGs expression in PAAD and normal tissues. Then, we constructed a protein-protein interaction (PPI) network of 23 ARGs using the search for interacting genes (strings). Functional enrichment analysis The R package for GO and KEGG statistical analysis through clusterProfiler applies biological term classification and enrichment analysis to gene cluster comparisons and provides a visualization of the results(Yu, Wang, Han, & He, 2012 ). Correlation analysis Spearman's correlation analysis was used to describe correlations between quantitative variables without a normal distribution. P-values less than 0.05 were considered statistically significant. Survival analysis Log rank was used to test the KM survival analysis to compare the survival differences between the two groups described above. For Kaplan-Meier curves, p-values and hazard ratios (HR) with 95% confidence intervals (CI) were derived by logrank test and univariate Cox regression. All of the above analytical methods and R packages were performed using v4.0.3 version of R software (R Foundation for Statistical Computing, 2020). P-values < 0.05 was considered statistically significant. Development of the anoikis-related signature A lasso regression-based approach was used to downscale and construct a prognostic model. The model is a riskscore formula containing multiple genes with weights for each gene, with negative numbers representing the gene as a protected gene and positive numbers representing the gene as a risk gene. lasso regression is where the coefficients of selected features are shown by lambda parameters, with the horizontal coordinate representing the value of the independent variable lambda and the vertical coordinate representing the coefficient of the independent variable. log rank is used to test KM survival analysis comparing the difference in survival between two or more groups as described above, a timeROC analysis was performed to discriminate the accuracy of the prediction model. For Kaplan-Meier curves, p-values and hazard ratios (HR) with 95% confidence intervals (CI) were derived by logrank test and univariate Cox regression. All the above analytical methods and R packages were performed using v4.0.3 version of R software (R Foundation for Statistical Computing, 2020). P-values < 0.05 was considered statistically significant. Cell culture Human normal pancreatic ductal epithelial cells (HPDE6-C7) and pancreatic cancer cells (SW1990, PANC-1) were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). HPDE6-C7 cells were cultured in DMEM (HyClone, Beijing, China) containing 10% FBS. SW1990 cells were cultured in L-15 (HyClone, Beijing, China) containing 10% fetal bovine serum (FBS) (Gibco, Carlsbad, CA). PANC-1 cells were cultured in DMEM (HyClone, Beijing, China) containing 10% FBS. The cell culture medium was mixed with 100 units/mL penicillin and 100 mg/mL streptomycin. Cells were cultured at 37°C in a humidified atmosphere with 5% CO2. siRNA transfection BCL2L1 mRNA expression was down-regulated using small interfering RNA (siRNA) and si-NC was used as a negative control. siRNA-BCL2L1 and si-NC were purchased from GenePharma (Suzhou, China). Transfection of siRNA (30 nM) was performed according to the manufacturer's recommendations, and the expression of BCL2L1 was confirmed to be down-regulated by qRT-PCR. qRT-PCR Total RNA was extracted using RNAiso Plus (Takara, Shiga, Japan) according to the manufacturer's steps, and RevertAid First Strand cDNA Synthesis kit (Thermo Fisher Scientific) was used for cDNA synthesis according to the manufacturer's steps. Levels of FN1 were quantified by IQ SYBR Premix Ex Taq Perfect Real-Time (Bio-Rad Laboratories, Hercules, CA) to perform real-time polymerase chain reaction (qRT-PCR) and tracked by β-actin. Primers were synthesized by Sangon Biotech (Sangon Biotech, Shang- hai, China). Relative miRNA level comparisons were calculated by the 2-ΔΔCT method. Cell Counting Kit-8 assay The effect of BCL2L1 on the proliferative capacity of pancreatic cancer cells was assessed by Cell Counting Kit-8 (CCK-8) assay. Cells were inoculated into a 96-well plate. Based on 1000 cells inoculated per well, each group has 6 wells and the wells without cells were used as blank control group. After adding Cell Counting Kit-8 (CCk-8) on days 1, 2, 3, 4, and 5, respectively, the cells were incubated in the dark of the incubator for 1 hour. The OD was measured by enzyme marker according to the wavelength of 490 nm. Results Studying the expression of ARGs in PAAD We first explored the expression of 23 ARGs in PAAD and normal tissues using the TCGA PAAD dataset, and a total of 23 ARGs were all up-regulated in PAAD with P-values < 0.05 (Fig. 1 A). Then correlation analysis among ARGs (Fig. 1 B) and protein-protein interaction (PPI) analysis were constructed to detect the interaction of these ARGs (Fig. 1 C). Functional enrichment analysis of ARGs To clarify the functions of ARGs, we analyzed these pathways using GO and KEGG databases. GO term enrichment results including biological pathway (BP), cellular component (CC) and molecular function (MF) and KEGG enrichment analysis results are shown in the Fig. 2 A-B. Construction of the anoikis-related signature To construct a prognostic gene model, one-way Cox regression analysis was used to screen ARGs with prognostic value. As a result, we identified all six genes with prognostic value, whose Kaplan-Meier survival curves are shown in Fig. 3 . The results suggested that the survival rate of PAAD with high expression of PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3 had lower survival rates (Fig. 3 A-F). LASSO Cox regression analysis was performed to construct prognostic gene models based on these in vitro prognostic ARGs (Fig. 4 A-B). Riskscore = (0.0385) * ITGB1+(0.1502) * EGFR+(0.0654) * CASP8+(0.2971) * BCL2L1. Patients with pancreatic cancer were divided into low-risk group and high-risk group based on riskscore. The distribution of riskscores, survival status and expression of these three genes are shown in Fig. 4 C. As the risk score increased, patients had an increased risk of death and decreased survival time. Kaplan-Meier curves showed that Pancreatic patients with high riskscores had worse overall survival probability than pancreatic patients with low riskscores (median time = for 1.3 years vs. 2 years, p = 0.00134, Fig. 4 D), and the AUC on the ROC curves at 1, 3 and 5 years 0.711, 0.738 and 0.678, respectively (Fig. 4 E). Validation of the anoikis-related signature The ARGs were analyzed and validated in the ICGC database, and the results showed that the overall survival rate was significantly lower in the high-risk group than in the low-risk group (median time = 1.2 years and 2.7, p = 1.83e-05 Fig. 5 A-B), and the AUC on the ROC curves at 1, 3 and 5 years 0.717, 0.738 and 0.832, respectively (Fig. 5 C). Construction of a predictive nomogram Considering the clinicopathological characteristics and these important prognostic ARGs, we also created a predictive column line graph to predict the probability of survival. Univariate and multifactorial analyses showed that BCL2L1 expression, pT stage, pN stage, and pM stage were independent factors affecting the prognosis of PAAD patients (Fig. 6 A-B). To further exploit the prognostic value of anoikis-related signature, we combined clinical factors and the anoikis-related signature to create a nomogram, but due to data limitations, only the 1-year and 2-year survival Prob were obtained (Fig. 6 C-D). ARGs were associated with immune infiltration of pancreatic cancer In our study, we also analyzed the correlation between the expression of ARGs (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3) and immune infiltration in PAAD using the TIMER database. The data showed that the expression of PTK2, ITGB1, EGFR, and CASP3 was positively correlated with the infiltration level of B cell, macrophage, medullary dendritic cell, neutrophil, and T cell CD8+. In contrast, BCL2L1 expression did not correlate with the immune infiltration level. In addition, CASP8 expression was positively correlated with the infiltration level of B cell, neutrophil, medullary dendritic cell, and T cell CD8+. Interestingly, the expression of ARGs did not correlate with T cell CD4 + infiltration level (Fig. 7 A). Then, we further analyzed the correlation between the signature's riskscore and various immune cells. The results showed a positive correlation between riskscore and the expression of B cell, T cell CD8+, neutrophil, and medullary dendritic cell, but no correlation with the expression of T cell CD4 + and macrophage (Fig. 7 B-J). This evidence suggests a significant correlation between ARGs and tumor immune infiltration. TMB and MSI analysis The correlation between ARGs and TMB as well as MSI in PAAD was analyzed. The results showed that TMB was positively correlated with the expression of CASP8, BCL2L1 and CASP3, but TMB was not significantly correlated with the expression of PTK2, ITGB1 and EGFR (Fig. 8 A). In MSI analysis, there was no significant correlation between MSI and ARGs (Fig. 8 B). Knockdown of BCL2L1 expression inhibited the proliferation of pancreatic cancer cells Firstly, the relative expression of BCL2L1 in human normal pancreatic ductal epithelial cell (HPDE6-C7) and pancreatic cancer cells (SW1990, PANC-1) was detected by qRT-PCR, and the results showed that the expression of BCL2L1 was significantly higher in SW1990 and PANC-1 than in HPDE6-C7 (Fig. 9 A). Then si-BCL2L1 was transfected in SW1990 and PANC-1. qRT-PCR assays showed that BCL2L1 expression was significantly knocked down in SW1990 and PANC-1 (Fig. 9 B). Finally, the cell proliferation ability of SW1990 and PANC-1 was examined by CCK-8 assay, and the results showed that the cell proliferation ability was inhibited after the expression of BCL2L1 was knocked down (Fig. 9 C-D). Discussion Anoikis is known as a specific type of programmed cell death, apoptosis caused by loss of cell adhesion or inappropriate cell adhesion. Dysplastic cell growth or cells attached to inappropriate substrates can be effectively removed by anoikis, thereby maintaining tissue homeostasis and development. However, during tumorigenesis, cancer cells often acquire resistance to anoikis and become more aggressive and metastatic, making anoikis a potential prognostic and therapeutic target for cancer(Malagobadan, Ho, & Nagoor, 2020 ). In endometrial cancer, novel ARGs signatures have been identified to predict prognosis(S. Chen, Gu, Zhang, Hu, & Ge, 2021 ). However, the role of ARGs in PAAD has not been elucidated, and our study aimed to elucidate this role. We first elucidated the screening for pancreatic anoikis-related genes and their prognostic value. We found that the expression of BRMS1, PTK2, BCL2L11, SRC, CEACAM6, CAV1, ITGB1, CEACAM5, EGFR, BCL2, CASP8, PTRH2, STAT3, CTNNB1, ZNF304, MAPK1, BMF, ITGA5, TP53, MCL1, BCL2L1, CASP3, and CDH1 was increased in PAAD compared with normal tissues. Prognostic analysis showed that pancreatic patients with high expression of PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3 had a lower survival rate. These data are consistent with previous results. Hayato Fujita et al. found that EGFR mRNA expression may help predict the prognosis and sensitivity of PDAC patients to gemcitabine(Fujita et al., 2011 ). We also performed a functional enrichment analysis of ARGs and found that these 23 ARGs are mainly involved in proteoglycans in cancer, regulation of anoikis, lipid and atherosclerosis, focal adhesion, regulation of apoptotic signaling pathway, EGFR tyrosine kinase inhibitor resistance, and human cytomegalovirus infection. Interestingly, these functions or pathways are involved in pancreatic carcinogenesis and progression. In addition, a previous study showed that Inhibition of focal adhesion kinase (FAK) alters the production of pro-inflammatory and immunosuppressive cytokines by pancreatic cancer cells and impairs their ability to avoid immune surveillance(H. Jiang et al., 2016 ). These results suggest that these 23 ARGs may also play an important role in the tumorigenesis and progression of PAAD. LASSO Cox regression analysis was used to construct a prognostic genetic model based on six prognostic ARGs (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3), which predicted overall survival of pancreatic patients with moderate to high precision. The predictive nomogram shows that overall survival at 1- and 2- year can be predicted relatively well compared to the ideal model in the entire cohort. A previous study identified several ferroptosis-related prognostic features in pancreatic cancer and validated the potential value of ferroptosis-related prognostic features in the personalized immunotherapy of pancreatic cancer(P. Jiang et al., 2021 ). It has been found that CD73 can be used as a prognostic marker that can promote pancreatic cancer progression as well as achieve immune escape by disrupting T-cell infiltration and immune response, which ultimately leads to poor prognosis(Q. Chen et al., 2020 ). In addition, a prognostic signature was constructed based on immune-related genes in pancreatic cancer, which showed better ability to predict 1-, 2- and 3-year survival in pancreatic cancer patients(Liu, Fu, He, Du, & Xu, 2021 ). In our study, we identified for the first time a prognostic gene signature associated with anoikis of pancreatic cancer, which provides additional options for prognostic prediction of Pancreatic. In our study, PTK2 was found to be one of the gene signatures. A previous study showed that PTK2 regulates cell cycle progression and plays an important role in cell proliferation and apoptosis. PTK2, also known as focal adhesion kinase 1, regulates cell migration and adhesion and is significantly associated with loss-of-nest apoptosis, thus being involved in cancer development and progression(Weisser et al., 2014 ; Zachary, 1997 ). In addition, the causal role of PTK2 in tumor development and its association with poor clinical outcome, PTK2 may be a useful target for tumor therapy(McLean et al., 2005 ). However, studies on the role of PTK2 in anoikis in pancreatic cancer are limited. In our study, we found that PTK2 is one of the prognostic biomarkers associated with anoikis in PAAD. We need to perform further in vivo and in vitro studies to verify whether PTK2 is involved in anoikis in pancreatic cancer. Another important finding of our study showed that the above prognostic genes associated with anoikis were significantly associated with immune infiltration, which further supports the fact that anoikis plays an important role in the tumor immune microenvironment. Interleukin-8 expression was found to be negatively associated with anoikis in colorectal cancer cells(Xiao et al., 2015 ). Anti-GD2 ganglioside antibodies can induce anoikis in cancer cells and thus treat human small cell lung cancer(Aixinjueluo et al., 2005 ). EGFR regulates the immune microenvironment of pancreatic cancer tumors, and its inhibitor erlotinib has been approved by the FDA for the treatment of pancreatic cancer(Li et al., 2021 ). Recently, ITGB1 has been identified as a potential biomarker for predicting gastric cancer patients who may benefit from immune checkpoint inhibitors(Gu et al., 2022 ). TMB is considered a potential biomarker for predicting the effect of immunotherapy. Our findings identified BCL2L1 as an independent risk factor for pancreatic cancer, while in past studies, BCL2L1 was found to promote the growth of colon cancer cells. However, we further explored the expression of BCL2L1 in pancreatic cancer cells and human normal pancreatic ductal epithelial cell and found that the expression of BCL2L1 in pancreatic cancer cells was significantly higher than that in human normal pancreatic ductal epithelial cells, and we constructed an interference plasmid for BCL2L1 and found that knocking down the expression of BCL2L1 in pancreatic cancer cells could inhibit the proliferation ability of the cells by CCK-8 assay. We also investigated the relationship between six prognostic ARGs and tumor mutational load, and microsatellite instability. Tumor mutational load (TMB), microsatellite instability (MSI) are current biomarkers of cancer immunotherapy effectiveness and are clinically important for patients with a wide range of solid tumors(Addeo, Friedlaender, Banna, & Weiss, 2021 ; Cortes-Ciriano, Lee, Park, Kim, & Park, 2017 ). Our final results showed that only the expression of CASP8, BCL2L1 and CASP3 correlated with TMB analysis. Conclusion We performed a comprehensive and systematic bioinformatic analysis of PAAD and identified anoikis-related prognostic gene signature containing six genes (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3) for pancreatic cancer patients. In vivo and in vitro experiments should be needed to further confirm our findings. Declarations Authors’ Note Wei Chen conceived, designed, and analyzed the data. Zeyan Xu and Jingjing Jiang wrote the manuscript. Hong Chen, and Ruihua Shi reviewed the data and revised the manuscript. All authors read and approved the final manuscript. Ethics Approval None to declare. Availability of Data and Materials All the data were extracted from TCGA dataset, GTEx database and GeneCards database. Funding Statement The study was funded by the Jiangsu Chinese Hospital Administration (NO: ZT202120). Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. References Addeo, A., Friedlaender, A., Banna, G. L., & Weiss, G. J. (2021). TMB or not TMB as a biomarker: That is the question. Crit Rev Oncol Hematol, 163 , 103374. doi: 10.1016/j.critrevonc.2021.103374 Aixinjueluo, W., Furukawa, K., Zhang, Q., Hamamura, K., Tokuda, N., Yoshida, S.,.. . Furukawa, K. (2005). Mechanisms for the apoptosis of small cell lung cancer cells induced by anti-GD2 monoclonal antibodies: roles of anoikis. J Biol Chem, 280 (33), 29828–29836. doi: 10.1074/jbc.M414041200 Cai, J., Chen, H., Lu, M., Zhang, Y., Lu, B., You, L.,.. . Zhao, Y. (2021). Advances in the epidemiology of pancreatic cancer: Trends, risk factors, screening, and prognosis. Cancer Lett, 520 , 1–11. doi: 10.1016/j.canlet.2021.06.027 Chen, Q., Pu, N., Yin, H., Zhang, J., Zhao, G., Lou, W., & Wu, W. (2020). CD73 acts as a prognostic biomarker and promotes progression and immune escape in pancreatic cancer. J Cell Mol Med, 24 (15), 8674–8686. doi: 10.1111/jcmm.15500 Chen, S., Gu, J., Zhang, Q., Hu, Y., & Ge, Y. (2021). Development of Biomarker Signatures Associated with Anoikis to Predict Prognosis in Endometrial Carcinoma Patients. J Oncol, 2021 , 3375297. doi: 10.1155/2021/3375297 Cortes-Ciriano, I., Lee, S., Park, W. Y., Kim, T. M., & Park, P. J. (2017). A molecular portrait of microsatellite instability across multiple cancers. Nat Commun, 8 , 15180. doi: 10.1038/ncomms15180 Fujita, H., Ohuchida, K., Mizumoto, K., Itaba, S., Ito, T., Nakata, K.,.. . Tanaka, M. (2011). High EGFR mRNA expression is a prognostic factor for reduced survival in pancreatic cancer after gemcitabine-based adjuvant chemotherapy. Int J Oncol, 38 (3), 629–641. doi: 10.3892/ijo.2011.908 Gu, W., Sun, H., Zhang, M., Mo, S., Tan, C., Ni, S.,.. . Wang, L. (2022). ITGB1 as a prognostic biomarker correlated with immune suppression in gastric cancer. Cancer Med. doi: 10.1002/cam4.5042 Jiang, H., Hegde, S., Knolhoff, B. L., Zhu, Y., Herndon, J. M., Meyer, M. A.,.. . DeNardo, D. G. (2016). Targeting focal adhesion kinase renders pancreatic cancers responsive to checkpoint immunotherapy. Nat Med, 22 (8), 851–860. doi: 10.1038/nm.4123 Jiang, P., Yang, F., Zou, C., Bao, T., Wu, M., Yang, D., & Bu, S. (2021). The construction and analysis of a ferroptosis-related gene prognostic signature for pancreatic cancer. Aging (Albany NY), 13 (7), 10396–10414. doi: 10.18632/aging.202801 Klein, A. P. (2021). Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol, 18 (7), 493–502. doi: 10.1038/s41575-021-00457-x Li, J., Yuan, S., Norgard, R. J., Yan, F., Sun, Y. H., Kim, I. K.,.. . Stanger, B. Z. (2021). Epigenetic and Transcriptional Control of the Epidermal Growth Factor Receptor Regulates the Tumor Immune Microenvironment in Pancreatic Cancer. Cancer Discov, 11 (3), 736–753. doi: 10.1158/2159-8290.CD-20-0519 Liu, B., Fu, T., He, P., Du, C., & Xu, K. (2021). Construction of a five-gene prognostic model based on immune-related genes for the prediction of survival in pancreatic cancer. Biosci Rep, 41 (7). doi: 10.1042/BSR20204301 Malagobadan, S., Ho, C. S., & Nagoor, N. H. (2020). MicroRNA-6744-5p promotes anoikis in breast cancer and directly targets NAT1 enzyme. Cancer Biol Med, 17 (1), 101–111. doi: 10.20892/j.issn.2095-3941.2019.0010 McLean, G. W., Carragher, N. O., Avizienyte, E., Evans, J., Brunton, V. G., & Frame, M. C. (2005). The role of focal-adhesion kinase in cancer - a new therapeutic opportunity. Nat Rev Cancer, 5 (7), 505–515. doi: 10.1038/nrc1647 Paoli, P., Giannoni, E., & Chiarugi, P. (2013). Anoikis molecular pathways and its role in cancer progression. Biochim Biophys Acta, 1833 (12), 3481–3498. doi: 10.1016/j.bbamcr.2013.06.026 Torphy, R. J., Fujiwara, Y., & Schulick, R. D. (2020). Pancreatic cancer treatment: better, but a long way to go. Surg Today, 50 (10), 1117–1125. doi: 10.1007/s00595-020-02028-0 Weisser, M., Yeh, R. F., Duchateau-Nguyen, G., Palermo, G., Nguyen, T. Q., Shi, X.,.. . Dornan, D. (2014). PTK2 expression and immunochemotherapy outcome in chronic lymphocytic leukemia. Blood, 124 (3), 420–425. doi: 10.1182/blood-2013-12-538975 Xiao, Y. C., Yang, Z. B., Cheng, X. S., Fang, X. B., Shen, T., Xia, C. F.,.. . Li, Y. F. (2015). CXCL8, overexpressed in colorectal cancer, enhances the resistance of colorectal cancer cells to anoikis. Cancer Lett, 361 (1), 22–32. doi: 10.1016/j.canlet.2015.02.021 Yu, G., Wang, L. G., Han, Y., & He, Q. Y. (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 16 (5), 284–287. doi: 10.1089/omi.2011.0118 Zachary, I. (1997). Focal adhesion kinase. Int J Biochem Cell Biol, 29 (7), 929–934. doi: 10.1016/s1357-2725(97)00008-3 Zhang, H. F., Hughes, C. S., Li, W., He, J. Z., Surdez, D., El-Naggar, A. M.,.. . Sorensen, P. H. (2021). Proteomic Screens for Suppressors of Anoikis Identify IL1RAP as a Promising Surface Target in Ewing Sarcoma. Cancer Discov, 11 (11), 2884–2903. doi: 10.1158/2159-8290.CD-20-1690 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2326682","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":156174195,"identity":"94fe2640-e0a9-4ca5-8792-5ad657d485eb","order_by":0,"name":"Wei Chen","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Chen","suffix":""},{"id":156174196,"identity":"e106de14-71d5-4f5b-8270-3264bb70c3f9","order_by":1,"name":"Zeyan Xu","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zeyan","middleName":"","lastName":"Xu","suffix":""},{"id":156174197,"identity":"d5cf6841-ba70-43ce-9a91-1795cd48146f","order_by":2,"name":"Jingjing Jiang","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Jiang","suffix":""},{"id":156174198,"identity":"653021e1-d85c-455c-914f-6d77c9ffa8b0","order_by":3,"name":"Hong Chen","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Chen","suffix":""},{"id":156174199,"identity":"31f3ab0f-e966-48aa-a0f2-a63e7f2d4b14","order_by":4,"name":"Ruihua Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACNmbGhgMJBjZy/OwNYAHGBkJa+NibGx88KEgzluw5QKQWOZ7jzYYPPhxO3HAjgUgtbBKJbRIJBoeNGW4+f/iYh8FGdsMB5mcPiNCSLsc4O8fYmIchzXjDATZzAyK0WBszS+ewSfMwAF14gIdNgggtzIltksef/+Zh+E+EFp6DzQYJBs6JPRIMZsw8DAeI0MLe2PggwSDNWIInx1hyjkGy8czDbGZ4tcg3sz84+OOPjZz98eMPP7ypsJPtO978DK8WNAAKKmYS1I+CUTAKRsEowA4A4nxITvgKpGcAAAAASUVORK5CYII=","orcid":"","institution":"Southeast University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ruihua","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2022-11-29 18:59:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2326682/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2326682/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29837616,"identity":"aad7c967-9c5c-4c31-82ba-e1368a5181f8","added_by":"auto","created_at":"2022-12-02 20:09:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":913795,"visible":true,"origin":"","legend":"\u003cp\u003eStudying the expression of ARGs in PAAD. (A) The expression of 23 ARGs in PAAD and normal tissues. The upper and lower ends of the boxes represented the interquartile range of values. The lines in the boxes represented median value. (B) Correlation analysis of 23 ARGs in PAAD. (C) protein-protein interaction (PPI) analysis of 23 ARGs in PAAD. ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ARGs anoikis-related genes, PAAD Pancreatic adenocarcinoma.\u003c/p\u003e","description":"","filename":"1fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-2326682/v1/ec37055713f122739481325f.png"},{"id":29837598,"identity":"43ad7b6c-ad8c-4808-92ed-376654d50e3a","added_by":"auto","created_at":"2022-12-02 20:09:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":465902,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of ARGs. (A) Gene ontology (GO) analysis of ARGs. (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of ARGs.\u003c/p\u003e","description":"","filename":"2fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-2326682/v1/04c8ff27fd011b7be5b092e1.png"},{"id":29837596,"identity":"f636aa55-6aa6-4955-b74f-182f0183eaf6","added_by":"auto","created_at":"2022-12-02 20:09:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":222509,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of prognostic differences according to ARGs expression. Effect of (A) PTK2, (B) ITGB1, (C) EGFR, (D) CASP8, (E) BCL2L1, and (F) CASP3 on survival rate in the high-/low-expression group.\u003c/p\u003e","description":"","filename":"3fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-2326682/v1/769496df5861824d3ad6656e.png"},{"id":29838162,"identity":"934600c0-1286-4d3c-83a6-3e610001111e","added_by":"auto","created_at":"2022-12-02 20:17:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":320604,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the anoikis-related signature. (A) LASSO coefficient of anoikis-related genes in PAAD. (B) LASSO regression analysis for the development of the anoikis-related signature. (C) Assessment of survival time, survival status according to Riskscore. (D) Overall survival curves of PAAD patients in high/low risk groups. (E) Relationship between ROC curves and AUC values at different times.\u003c/p\u003e","description":"","filename":"4fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-2326682/v1/cb6653c2c9c9018395a3ab26.png"},{"id":29837610,"identity":"a0d2f8f1-7ea3-4cab-89d2-2b199f0da446","added_by":"auto","created_at":"2022-12-02 20:09:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":242902,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the anoikis-related signature. (A) Assessment of survival time, survival status according to Riskscore based on ICGC dataset. (B) Overall survival curves of PAAD patients in high/low risk groups ICGC dataset. (C) Relationship between ROC curves and AUC values at different times ICGC dataset.\u003c/p\u003e","description":"","filename":"5fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-2326682/v1/76f60965456c92eda20e6cc5.png"},{"id":29838159,"identity":"3ca199ed-1a00-42f1-bc5b-5135f313bd22","added_by":"auto","created_at":"2022-12-02 20:17:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":185147,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of a predictive nomogram. (A) Univariate cox analysis of ARGs and prognosis. (B) Multivariate cox analysis of ARGs and prognosis. (C) A nomogram obtained based on the anoikis-related signature. (D) Calibration curve for the overall survival nomogram model in the discovery group.\u003c/p\u003e","description":"","filename":"6fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-2326682/v1/a7087f29de32b14626eb0704.png"},{"id":29837619,"identity":"65e063df-3315-4bc3-ac8f-9cc2b737dce1","added_by":"auto","created_at":"2022-12-02 20:09:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":307520,"visible":true,"origin":"","legend":"\u003cp\u003eARGs were associated with immune infiltration of pancreatic cancer. (A) Heat map of correlation between ARGs and immune cells. (B) Correlation of signature prognostic model of riskscore with various immune cells. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"7fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-2326682/v1/aaaf9dba739934a7c80b42df.png"},{"id":29837626,"identity":"9e1175ed-edb7-4929-8474-7be0ab312a07","added_by":"auto","created_at":"2022-12-02 20:09:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":234851,"visible":true,"origin":"","legend":"\u003cp\u003eTMB and MSI analysis. (A) Correlation of ARGs with TMB. (B) Correlation of ARGs with MSI.\u003c/p\u003e","description":"","filename":"8fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-2326682/v1/c8cfa2c83e262422987f3a19.png"},{"id":29837628,"identity":"c496f420-e3a7-4371-847a-6bfdb9297fac","added_by":"auto","created_at":"2022-12-02 20:09:24","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":127160,"visible":true,"origin":"","legend":"\u003cp\u003eKnockdown of BCL2L1 expression inhibited PC cells proliferation. (A) Relative levels of BCL2L1 were analyzed in HPDE6-C7, SW1990, and PANC-1. (B) The interference efficiency of sh-BCL2L1was analyzed in SW1990 and PANC-1. (C) The cell proliferation rate of SW1990 was assessed by the CCK-8 assay. (D) The cell proliferation rate of PANC-1 was assessed by the CCK-8 assay. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"9fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-2326682/v1/e6f0c7dea2676da08ec55644.png"},{"id":32979623,"identity":"8ac4337d-b5e0-47c7-aae5-7dde4ece9ce1","added_by":"auto","created_at":"2023-02-15 14:00:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2379726,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2326682/v1/707f8d06-3f86-4f9a-a74b-21efd904833b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of the anoikis-related prognostic gene signature in pancreatic adenocarcinoma","fulltext":[{"header":"Introducion","content":"\u003cp\u003ePancreatic adenocarcinoma (PAAD) is a common malignant tumor with a very high degree of malignancy(Klein, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The main treatment methods currently available are surgery, chemotherapy and radiation therapy(Torphy, Fujiwara, \u0026amp; Schulick, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Early surgical resection is currently the only effective treatment. However, because early detection and diagnosis of pancreatic cancer is very difficult, surgical opportunities are often missed and lead to poor prognosis(Cai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, it becomes extremely important to reveal the genetic profile of pancreatic cancer prognosis and to develop new approaches for the treatment of pancreatic cancer.\u003c/p\u003e \u003cp\u003eAnoikis, a programmed cell death, is induced when cells are detached from the extracellular matrix and is a key mechanism to prevent the growth and attachment of apposition-independent cells to inappropriate matrices, thereby avoiding distal organ colonization. Cancer cells resist anoikis through two major pathways, anchorage-independent growth and epithelial-mesenchymal transition, thereby promoting cancer progression and metastasis.(Paoli, Giannoni, \u0026amp; Chiarugi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) Cancer cells must overcome anoikis (detachment-induced death) to successfully metastasize. It has been found that high expression of IL1RAP in Pancreatic can inhibit anoikis to promote cancer progression(Zhang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, it is important to further explore the mechanism of the role of anoikis in the development of pancreatic cancer.\u003c/p\u003e \u003cp\u003eIn this study, we used bioinformatics analysis to investigate ARGs expression of proprotein and its prognostic significance as well as immune infiltration. Our data may provide additional evidence for prognostic biomarkers and therapeutic targets for pancreatic cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData Collection\u003c/p\u003e\n\u003cp\u003eRNA-seq transcriptomic data and corresponding clinical medical information of pancreatic cancer patients were obtained from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003c/span\u003e). A total of 179 Pancreatic tumor samples and 4 adjacent normal samples were included in our study, and supplemented with RNA transcriptome data from the GTEx database of 328 healthy human normal tissue samples. ARGs were obtained from the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003c/span\u003e). The \u0026quot;limma\u0026quot; and \u0026quot;remodeling2\u0026quot; software packages were used to identify differences in ARGs expression in PAAD and normal tissues. Then, we constructed a protein-protein interaction (PPI) network of 23 ARGs using the search for interacting genes (strings).\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analysis\u003c/p\u003e\n\u003cp\u003eThe R package for GO and KEGG statistical analysis through clusterProfiler applies biological term classification and enrichment analysis to gene cluster comparisons and provides a visualization of the results(Yu, Wang, Han, \u0026amp; He, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eCorrelation analysis\u003c/p\u003e\n\u003cp\u003eSpearman\u0026apos;s correlation analysis was used to describe correlations between quantitative variables without a normal distribution. P-values less than 0.05 were considered statistically significant.\u003c/p\u003e\n\u003cp\u003eSurvival analysis\u003c/p\u003e\n\u003cp\u003eLog rank was used to test the KM survival analysis to compare the survival differences between the two groups described above. For Kaplan-Meier curves, p-values and hazard ratios (HR) with 95% confidence intervals (CI) were derived by logrank test and univariate Cox regression. All of the above analytical methods and R packages were performed using v4.0.3 version of R software (R Foundation for Statistical Computing, 2020). P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003eDevelopment of the anoikis-related signature\u003c/p\u003e\n\u003cp\u003eA lasso regression-based approach was used to downscale and construct a prognostic model. The model is a riskscore formula containing multiple genes with weights for each gene, with negative numbers representing the gene as a protected gene and positive numbers representing the gene as a risk gene. lasso regression is where the coefficients of selected features are shown by lambda parameters, with the horizontal coordinate representing the value of the independent variable lambda and the vertical coordinate representing the coefficient of the independent variable. log rank is used to test KM survival analysis comparing the difference in survival between two or more groups as described above, a timeROC analysis was performed to discriminate the accuracy of the prediction model. For Kaplan-Meier curves, p-values and hazard ratios (HR) with 95% confidence intervals (CI) were derived by logrank test and univariate Cox regression. All the above analytical methods and R packages were performed using v4.0.3 version of R software (R Foundation for Statistical Computing, 2020). P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003eCell culture\u003c/p\u003e\n\u003cp\u003eHuman normal pancreatic ductal epithelial cells (HPDE6-C7) and pancreatic cancer cells (SW1990, PANC-1) were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). HPDE6-C7 cells were cultured in DMEM (HyClone, Beijing, China) containing 10% FBS. SW1990 cells were cultured in L-15 (HyClone, Beijing, China) containing 10% fetal bovine serum (FBS) (Gibco, Carlsbad, CA). PANC-1 cells were cultured in DMEM (HyClone, Beijing, China) containing 10% FBS. The cell culture medium was mixed with 100 units/mL penicillin and 100 mg/mL streptomycin. Cells were cultured at 37\u0026deg;C in a humidified atmosphere with 5% CO2.\u003c/p\u003e\n\u003cp\u003esiRNA transfection\u003c/p\u003e\n\u003cp\u003eBCL2L1 mRNA expression was down-regulated using small interfering RNA (siRNA) and si-NC was used as a negative control. siRNA-BCL2L1 and si-NC were purchased from GenePharma (Suzhou, China). Transfection of siRNA (30 nM) was performed according to the manufacturer\u0026apos;s recommendations, and the expression of BCL2L1 was confirmed to be down-regulated by qRT-PCR.\u003c/p\u003e\n\u003cp\u003eqRT-PCR\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted using RNAiso Plus (Takara, Shiga, Japan) according to the manufacturer\u0026apos;s steps, and RevertAid First Strand cDNA Synthesis kit (Thermo Fisher Scientific) was used for cDNA synthesis according to the manufacturer\u0026apos;s steps. Levels of FN1 were quantified by IQ SYBR Premix Ex Taq Perfect Real-Time (Bio-Rad Laboratories, Hercules, CA) to perform real-time polymerase chain reaction (qRT-PCR) and tracked by \u0026beta;-actin. Primers were synthesized by Sangon Biotech (Sangon Biotech, Shang- hai, China). Relative miRNA level comparisons were calculated by the 2-\u0026Delta;\u0026Delta;CT method.\u003c/p\u003e\n\u003cp\u003eCell Counting Kit-8 assay\u003c/p\u003e\n\u003cp\u003eThe effect of BCL2L1 on the proliferative capacity of pancreatic cancer cells was assessed by Cell Counting Kit-8 (CCK-8) assay. Cells were inoculated into a 96-well plate. Based on 1000 cells inoculated per well, each group has 6 wells and the wells without cells were used as blank control group. After adding Cell Counting Kit-8 (CCk-8) on days 1, 2, 3, 4, and 5, respectively, the cells were incubated in the dark of the incubator for 1 hour. The OD was measured by enzyme marker according to the wavelength of 490 nm.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eStudying the expression of ARGs in PAAD\u003c/p\u003e\n\u003cp\u003eWe first explored the expression of 23 ARGs in PAAD and normal tissues using the TCGA PAAD dataset, and a total of 23 ARGs were all up-regulated in PAAD with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Then correlation analysis among ARGs (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB) and protein-protein interaction (PPI) analysis were constructed to detect the interaction of these ARGs (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analysis of ARGs\u003c/p\u003e\n\u003cp\u003eTo clarify the functions of ARGs, we analyzed these pathways using GO and KEGG databases. GO term enrichment results including biological pathway (BP), cellular component (CC) and molecular function (MF) and KEGG enrichment analysis results are shown in the Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-B.\u003c/p\u003e\n\u003cp\u003eConstruction of the anoikis-related signature\u003c/p\u003e\n\u003cp\u003eTo construct a prognostic gene model, one-way Cox regression analysis was used to screen ARGs with prognostic value. As a result, we identified all six genes with prognostic value, whose Kaplan-Meier survival curves are shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The results suggested that the survival rate of PAAD with high expression of PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3 had lower survival rates (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-F). LASSO Cox regression analysis was performed to construct prognostic gene models based on these in vitro prognostic ARGs (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). Riskscore = (0.0385) * ITGB1+(0.1502) * EGFR+(0.0654) * CASP8+(0.2971) * BCL2L1. Patients with pancreatic cancer were divided into low-risk group and high-risk group based on riskscore. The distribution of riskscores, survival status and expression of these three genes are shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC. As the risk score increased, patients had an increased risk of death and decreased survival time. Kaplan-Meier curves showed that Pancreatic patients with high riskscores had worse overall survival probability than pancreatic patients with low riskscores (median time\u0026thinsp;=\u0026thinsp;for 1.3 years vs. 2 years, p\u0026thinsp;=\u0026thinsp;0.00134, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD), and the AUC on the ROC curves at 1, 3 and 5 years 0.711, 0.738 and 0.678, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eValidation of the anoikis-related signature\u003c/p\u003e\n\u003cp\u003eThe ARGs were analyzed and validated in the ICGC database, and the results showed that the overall survival rate was significantly lower in the high-risk group than in the low-risk group (median time\u0026thinsp;=\u0026thinsp;1.2 years and 2.7, p\u0026thinsp;=\u0026thinsp;1.83e-05 Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA-B), and the AUC on the ROC curves at 1, 3 and 5 years 0.717, 0.738 and 0.832, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eConstruction of a predictive nomogram\u003c/p\u003e\n\u003cp\u003eConsidering the clinicopathological characteristics and these important prognostic ARGs, we also created a predictive column line graph to predict the probability of survival. Univariate and multifactorial analyses showed that BCL2L1 expression, pT stage, pN stage, and pM stage were independent factors affecting the prognosis of PAAD patients (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). To further exploit the prognostic value of anoikis-related signature, we combined clinical factors and the anoikis-related signature to create a nomogram, but due to data limitations, only the 1-year and 2-year survival Prob were obtained (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC-D).\u003c/p\u003e\n\u003cp\u003eARGs were associated with immune infiltration of pancreatic cancer\u003c/p\u003e\n\u003cp\u003eIn our study, we also analyzed the correlation between the expression of ARGs (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3) and immune infiltration in PAAD using the TIMER database. The data showed that the expression of PTK2, ITGB1, EGFR, and CASP3 was positively correlated with the infiltration level of B cell, macrophage, medullary dendritic cell, neutrophil, and T cell CD8+. In contrast, BCL2L1 expression did not correlate with the immune infiltration level. In addition, CASP8 expression was positively correlated with the infiltration level of B cell, neutrophil, medullary dendritic cell, and T cell CD8+. Interestingly, the expression of ARGs did not correlate with T cell CD4\u0026thinsp;+\u0026thinsp;infiltration level (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). Then, we further analyzed the correlation between the signature\u0026apos;s riskscore and various immune cells. The results showed a positive correlation between riskscore and the expression of B cell, T cell CD8+, neutrophil, and medullary dendritic cell, but no correlation with the expression of T cell CD4\u0026thinsp;+\u0026thinsp;and macrophage (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB-J). This evidence suggests a significant correlation between ARGs and tumor immune infiltration.\u003c/p\u003e\n\u003cp\u003eTMB and MSI analysis\u003c/p\u003e\n\u003cp\u003eThe correlation between ARGs and TMB as well as MSI in PAAD was analyzed. The results showed that TMB was positively correlated with the expression of CASP8, BCL2L1 and CASP3, but TMB was not significantly correlated with the expression of PTK2, ITGB1 and EGFR (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). In MSI analysis, there was no significant correlation between MSI and ARGs (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eKnockdown of BCL2L1 expression inhibited the proliferation of pancreatic cancer cells\u003c/p\u003e\n\u003cp\u003eFirstly, the relative expression of BCL2L1 in human normal pancreatic ductal epithelial cell (HPDE6-C7) and pancreatic cancer cells (SW1990, PANC-1) was detected by qRT-PCR, and the results showed that the expression of BCL2L1 was significantly higher in SW1990 and PANC-1 than in HPDE6-C7 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA). Then si-BCL2L1 was transfected in SW1990 and PANC-1. qRT-PCR assays showed that BCL2L1 expression was significantly knocked down in SW1990 and PANC-1 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eB). Finally, the cell proliferation ability of SW1990 and PANC-1 was examined by CCK-8 assay, and the results showed that the cell proliferation ability was inhibited after the expression of BCL2L1 was knocked down (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eC-D).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAnoikis is known as a specific type of programmed cell death, apoptosis caused by loss of cell adhesion or inappropriate cell adhesion. Dysplastic cell growth or cells attached to inappropriate substrates can be effectively removed by anoikis, thereby maintaining tissue homeostasis and development. However, during tumorigenesis, cancer cells often acquire resistance to anoikis and become more aggressive and metastatic, making anoikis a potential prognostic and therapeutic target for cancer(Malagobadan, Ho, \u0026amp; Nagoor, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In endometrial cancer, novel ARGs signatures have been identified to predict prognosis(S. Chen, Gu, Zhang, Hu, \u0026amp; Ge, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the role of ARGs in PAAD has not been elucidated, and our study aimed to elucidate this role.\u003c/p\u003e \u003cp\u003eWe first elucidated the screening for pancreatic anoikis-related genes and their prognostic value. We found that the expression of BRMS1, PTK2, BCL2L11, SRC, CEACAM6, CAV1, ITGB1, CEACAM5, EGFR, BCL2, CASP8, PTRH2, STAT3, CTNNB1, ZNF304, MAPK1, BMF, ITGA5, TP53, MCL1, BCL2L1, CASP3, and CDH1 was increased in PAAD compared with normal tissues.\u003c/p\u003e \u003cp\u003ePrognostic analysis showed that pancreatic patients with high expression of PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3 had a lower survival rate. These data are consistent with previous results. Hayato Fujita et al. found that EGFR mRNA expression may help predict the prognosis and sensitivity of PDAC patients to gemcitabine(Fujita et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). We also performed a functional enrichment analysis of ARGs and found that these 23 ARGs are mainly involved in proteoglycans in cancer, regulation of anoikis, lipid and atherosclerosis, focal adhesion, regulation of apoptotic signaling pathway, EGFR tyrosine kinase inhibitor resistance, and human cytomegalovirus infection. Interestingly, these functions or pathways are involved in pancreatic carcinogenesis and progression. In addition, a previous study showed that Inhibition of focal adhesion kinase (FAK) alters the production of pro-inflammatory and immunosuppressive cytokines by pancreatic cancer cells and impairs their ability to avoid immune surveillance(H. Jiang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These results suggest that these 23 ARGs may also play an important role in the tumorigenesis and progression of PAAD.\u003c/p\u003e \u003cp\u003eLASSO Cox regression analysis was used to construct a prognostic genetic model based on six prognostic ARGs (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3), which predicted overall survival of pancreatic patients with moderate to high precision. The predictive nomogram shows that overall survival at 1- and 2- year can be predicted relatively well compared to the ideal model in the entire cohort. A previous study identified several ferroptosis-related prognostic features in pancreatic cancer and validated the potential value of ferroptosis-related prognostic features in the personalized immunotherapy of pancreatic cancer(P. Jiang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It has been found that CD73 can be used as a prognostic marker that can promote pancreatic cancer progression as well as achieve immune escape by disrupting T-cell infiltration and immune response, which ultimately leads to poor prognosis(Q. Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, a prognostic signature was constructed based on immune-related genes in pancreatic cancer, which showed better ability to predict 1-, 2- and 3-year survival in pancreatic cancer patients(Liu, Fu, He, Du, \u0026amp; Xu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, we identified for the first time a prognostic gene signature associated with anoikis of pancreatic cancer, which provides additional options for prognostic prediction of Pancreatic.\u003c/p\u003e \u003cp\u003eIn our study, PTK2 was found to be one of the gene signatures. A previous study showed that PTK2 regulates cell cycle progression and plays an important role in cell proliferation and apoptosis. PTK2, also known as focal adhesion kinase 1, regulates cell migration and adhesion and is significantly associated with loss-of-nest apoptosis, thus being involved in cancer development and progression(Weisser et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zachary, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In addition, the causal role of PTK2 in tumor development and its association with poor clinical outcome, PTK2 may be a useful target for tumor therapy(McLean et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, studies on the role of PTK2 in anoikis in pancreatic cancer are limited. In our study, we found that PTK2 is one of the prognostic biomarkers associated with anoikis in PAAD. We need to perform further in vivo and in vitro studies to verify whether PTK2 is involved in anoikis in pancreatic cancer.\u003c/p\u003e \u003cp\u003eAnother important finding of our study showed that the above prognostic genes associated with anoikis were significantly associated with immune infiltration, which further supports the fact that anoikis plays an important role in the tumor immune microenvironment. Interleukin-8 expression was found to be negatively associated with anoikis in colorectal cancer cells(Xiao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Anti-GD2 ganglioside antibodies can induce anoikis in cancer cells and thus treat human small cell lung cancer(Aixinjueluo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). EGFR regulates the immune microenvironment of pancreatic cancer tumors, and its inhibitor erlotinib has been approved by the FDA for the treatment of pancreatic cancer(Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, ITGB1 has been identified as a potential biomarker for predicting gastric cancer patients who may benefit from immune checkpoint inhibitors(Gu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). TMB is considered a potential biomarker for predicting the effect of immunotherapy.\u003c/p\u003e \u003cp\u003eOur findings identified BCL2L1 as an independent risk factor for pancreatic cancer, while in past studies, BCL2L1 was found to promote the growth of colon cancer cells. However, we further explored the expression of BCL2L1 in pancreatic cancer cells and human normal pancreatic ductal epithelial cell and found that the expression of BCL2L1 in pancreatic cancer cells was significantly higher than that in human normal pancreatic ductal epithelial cells, and we constructed an interference plasmid for BCL2L1 and found that knocking down the expression of BCL2L1 in pancreatic cancer cells could inhibit the proliferation ability of the cells by CCK-8 assay.\u003c/p\u003e \u003cp\u003eWe also investigated the relationship between six prognostic ARGs and tumor mutational load, and microsatellite instability. Tumor mutational load (TMB), microsatellite instability (MSI) are current biomarkers of cancer immunotherapy effectiveness and are clinically important for patients with a wide range of solid tumors(Addeo, Friedlaender, Banna, \u0026amp; Weiss, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cortes-Ciriano, Lee, Park, Kim, \u0026amp; Park, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our final results showed that only the expression of CASP8, BCL2L1 and CASP3 correlated with TMB analysis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe performed a comprehensive and systematic bioinformatic analysis of PAAD and identified anoikis-related prognostic gene signature containing six genes (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3) for pancreatic cancer patients. In vivo and in vitro experiments should be needed to further confirm our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWei Chen conceived, designed, and analyzed the data.\u0026nbsp;Zeyan Xu and Jingjing Jiang\u0026nbsp;wrote the manuscript.\u0026nbsp;Hong Chen,\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand Ruihua Shi\u0026nbsp;reviewed the data and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data were extracted from TCGA dataset,\u0026nbsp;GTEx database and GeneCards database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the Jiangsu Chinese Hospital Administration (NO: ZT202120).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Conflicting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAddeo, A., Friedlaender, A., Banna, G. L., \u0026amp; Weiss, G. J. (2021). TMB or not TMB as a biomarker: That is the question. Crit Rev Oncol Hematol, \u003cem\u003e163\u003c/em\u003e, 103374. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.critrevonc.2021.103374\u003c/span\u003e\u003cspan address=\"10.1016/j.critrevonc.2021.103374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAixinjueluo, W., Furukawa, K., Zhang, Q., Hamamura, K., Tokuda, N., Yoshida, S.,.. . Furukawa, K. (2005). Mechanisms for the apoptosis of small cell lung cancer cells induced by anti-GD2 monoclonal antibodies: roles of anoikis. J Biol Chem, \u003cem\u003e280\u003c/em\u003e(33), 29828\u0026ndash;29836. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M414041200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M414041200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai, J., Chen, H., Lu, M., Zhang, Y., Lu, B., You, L.,.. . Zhao, Y. (2021). Advances in the epidemiology of pancreatic cancer: Trends, risk factors, screening, and prognosis. Cancer Lett, \u003cem\u003e520\u003c/em\u003e, 1\u0026ndash;11. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.canlet.2021.06.027\u003c/span\u003e\u003cspan address=\"10.1016/j.canlet.2021.06.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Q., Pu, N., Yin, H., Zhang, J., Zhao, G., Lou, W., \u0026amp; Wu, W. (2020). CD73 acts as a prognostic biomarker and promotes progression and immune escape in pancreatic cancer. J Cell Mol Med, \u003cem\u003e24\u003c/em\u003e(15), 8674\u0026ndash;8686. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jcmm.15500\u003c/span\u003e\u003cspan address=\"10.1111/jcmm.15500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, S., Gu, J., Zhang, Q., Hu, Y., \u0026amp; Ge, Y. (2021). Development of Biomarker Signatures Associated with Anoikis to Predict Prognosis in Endometrial Carcinoma Patients. \u003cem\u003eJ Oncol, 2021\u003c/em\u003e, 3375297. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2021/3375297\u003c/span\u003e\u003cspan address=\"10.1155/2021/3375297\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortes-Ciriano, I., Lee, S., Park, W. Y., Kim, T. M., \u0026amp; Park, P. J. (2017). A molecular portrait of microsatellite instability across multiple cancers. Nat Commun, \u003cem\u003e8\u003c/em\u003e, 15180. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ncomms15180\u003c/span\u003e\u003cspan address=\"10.1038/ncomms15180\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujita, H., Ohuchida, K., Mizumoto, K., Itaba, S., Ito, T., Nakata, K.,.. . Tanaka, M. (2011). High EGFR mRNA expression is a prognostic factor for reduced survival in pancreatic cancer after gemcitabine-based adjuvant chemotherapy. Int J Oncol, \u003cem\u003e38\u003c/em\u003e(3), 629\u0026ndash;641. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/ijo.2011.908\u003c/span\u003e\u003cspan address=\"10.3892/ijo.2011.908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu, W., Sun, H., Zhang, M., Mo, S., Tan, C., Ni, S.,.. . Wang, L. (2022). ITGB1 as a prognostic biomarker correlated with immune suppression in gastric cancer. Cancer Med. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cam4.5042\u003c/span\u003e\u003cspan address=\"10.1002/cam4.5042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, H., Hegde, S., Knolhoff, B. L., Zhu, Y., Herndon, J. M., Meyer, M. A.,.. . DeNardo, D. G. (2016). Targeting focal adhesion kinase renders pancreatic cancers responsive to checkpoint immunotherapy. Nat Med, \u003cem\u003e22\u003c/em\u003e(8), 851\u0026ndash;860. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nm.4123\u003c/span\u003e\u003cspan address=\"10.1038/nm.4123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, P., Yang, F., Zou, C., Bao, T., Wu, M., Yang, D., \u0026amp; Bu, S. (2021). The construction and analysis of a ferroptosis-related gene prognostic signature for pancreatic cancer. Aging (Albany NY), \u003cem\u003e13\u003c/em\u003e(7), 10396\u0026ndash;10414. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/aging.202801\u003c/span\u003e\u003cspan address=\"10.18632/aging.202801\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein, A. P. (2021). Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol, \u003cem\u003e18\u003c/em\u003e(7), 493\u0026ndash;502. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41575-021-00457-x\u003c/span\u003e\u003cspan address=\"10.1038/s41575-021-00457-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J., Yuan, S., Norgard, R. J., Yan, F., Sun, Y. H., Kim, I. K.,.. . Stanger, B. Z. (2021). Epigenetic and Transcriptional Control of the Epidermal Growth Factor Receptor Regulates the Tumor Immune Microenvironment in Pancreatic Cancer. Cancer Discov, \u003cem\u003e11\u003c/em\u003e(3), 736\u0026ndash;753. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/2159-8290.CD-20-0519\u003c/span\u003e\u003cspan address=\"10.1158/2159-8290.CD-20-0519\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, B., Fu, T., He, P., Du, C., \u0026amp; Xu, K. (2021). Construction of a five-gene prognostic model based on immune-related genes for the prediction of survival in pancreatic cancer. Biosci Rep, \u003cem\u003e41\u003c/em\u003e(7). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1042/BSR20204301\u003c/span\u003e\u003cspan address=\"10.1042/BSR20204301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalagobadan, S., Ho, C. S., \u0026amp; Nagoor, N. H. (2020). MicroRNA-6744-5p promotes anoikis in breast cancer and directly targets NAT1 enzyme. Cancer Biol Med, \u003cem\u003e17\u003c/em\u003e(1), 101\u0026ndash;111. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20892/j.issn.2095-3941.2019.0010\u003c/span\u003e\u003cspan address=\"10.20892/j.issn.2095-3941.2019.0010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLean, G. W., Carragher, N. O., Avizienyte, E., Evans, J., Brunton, V. G., \u0026amp; Frame, M. C. (2005). The role of focal-adhesion kinase in cancer - a new therapeutic opportunity. Nat Rev Cancer, \u003cem\u003e5\u003c/em\u003e(7), 505\u0026ndash;515. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrc1647\u003c/span\u003e\u003cspan address=\"10.1038/nrc1647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaoli, P., Giannoni, E., \u0026amp; Chiarugi, P. (2013). Anoikis molecular pathways and its role in cancer progression. Biochim Biophys Acta, \u003cem\u003e1833\u003c/em\u003e(12), 3481\u0026ndash;3498. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbamcr.2013.06.026\u003c/span\u003e\u003cspan address=\"10.1016/j.bbamcr.2013.06.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorphy, R. J., Fujiwara, Y., \u0026amp; Schulick, R. D. (2020). Pancreatic cancer treatment: better, but a long way to go. Surg Today, \u003cem\u003e50\u003c/em\u003e(10), 1117\u0026ndash;1125. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00595-020-02028-0\u003c/span\u003e\u003cspan address=\"10.1007/s00595-020-02028-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeisser, M., Yeh, R. F., Duchateau-Nguyen, G., Palermo, G., Nguyen, T. Q., Shi, X.,.. . Dornan, D. (2014). PTK2 expression and immunochemotherapy outcome in chronic lymphocytic leukemia. Blood, \u003cem\u003e124\u003c/em\u003e(3), 420\u0026ndash;425. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2013-12-538975\u003c/span\u003e\u003cspan address=\"10.1182/blood-2013-12-538975\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao, Y. C., Yang, Z. B., Cheng, X. S., Fang, X. B., Shen, T., Xia, C. F.,.. . Li, Y. F. (2015). CXCL8, overexpressed in colorectal cancer, enhances the resistance of colorectal cancer cells to anoikis. Cancer Lett, \u003cem\u003e361\u003c/em\u003e(1), 22\u0026ndash;32. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.canlet.2015.02.021\u003c/span\u003e\u003cspan address=\"10.1016/j.canlet.2015.02.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, G., Wang, L. G., Han, Y., \u0026amp; He, Q. Y. (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, \u003cem\u003e16\u003c/em\u003e(5), 284\u0026ndash;287. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/omi.2011.0118\u003c/span\u003e\u003cspan address=\"10.1089/omi.2011.0118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZachary, I. (1997). Focal adhesion kinase. Int J Biochem Cell Biol, \u003cem\u003e29\u003c/em\u003e(7), 929\u0026ndash;934. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1357-2725(97)00008-3\u003c/span\u003e\u003cspan address=\"10.1016/s1357-2725(97)00008-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, H. F., Hughes, C. S., Li, W., He, J. Z., Surdez, D., El-Naggar, A. M.,.. . Sorensen, P. H. (2021). Proteomic Screens for Suppressors of Anoikis Identify IL1RAP as a Promising Surface Target in Ewing Sarcoma. Cancer Discov, \u003cem\u003e11\u003c/em\u003e(11), 2884\u0026ndash;2903. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/2159-8290.CD-20-1690\u003c/span\u003e\u003cspan address=\"10.1158/2159-8290.CD-20-1690\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2326682/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2326682/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePancreatic adenocarcinoma (PAAD) remains a tumor with high malignancy and poor prognosis. Anoikis can regulate tumor cell invasion and metastasis, which in turn leads to poor prognosis for cancer patients. However, the role of anoikis-related genes (ARGs) in PAAD remains unclear. In our study, we performed a comprehensive bioinformatics analysis to construct a prognostic gene model. Correlations between ARGs and tumor immune infiltration, tumor mutation burden and microsatellite instability were assessed using Spearman correlation analysis. A total of 23 ARGs were upregulated in PAAD. Functional enrichment analysis revealed that these 23 ARGs were mainly involved in proteoglycans in cancer, regulation of anoikis, lipid and atherosclerosis, focal adhesion, and regulation of apoptotic Prognostic analysis showed that patients with PAAD with high expression of PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3 had a low survival rate. The prognostic ARGs model constructed using the above six prognostic genes was constructed to predict the overall survival of PAAD patients. There was a significant correlation between prognostic ARGs and immune cell infiltration and tumor mutation burden, while there was no correlation with microsatellite instability. Meanwhile, knockdown of BCL2L1 expression could inhibit the proliferation of pancreatic cancer cells. In conclusion, we performed a comprehensive bioinformatics analysis to identify PAAD patients containing six genes (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3). Further studies are needed to validate our findings.\u003c/p\u003e","manuscriptTitle":"Identification of the anoikis-related prognostic gene signature in pancreatic adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-02 20:06:42","doi":"10.21203/rs.3.rs-2326682/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef99a590-12b5-4f8b-b6c8-045cb5a9b9df","owner":[],"postedDate":"December 2nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-02-15T13:59:52+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-02 20:06:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2326682","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2326682","identity":"rs-2326682","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}