Skip to main content

Advertisement

SpringerLink
Log in

Development and Validation of an Inflammatory Response-Related Gene Signature for Predicting the Prognosis of Pancreatic Adenocarcinoma

  • Original Article
  • Published:
Inflammation Aims and scope Submit manuscript

Abstract

Pancreatic adenocarcinoma (PAAD) is a highly dangerous malignant tumor of the digestive tract, and difficult to diagnose, treat, and predict the prognosis. As we all know, tumor and inflammation can affect each other, and thus the inflammatory response in the microenvironment can be used to affect the prognosis. So far, the prognostic value of inflammatory response-related genes in PAAD is still unclear. Therefore, this study aimed to explore the inflammatory response-related genes for predicting the prognosis of PAAD. In this study, the mRNA expression profiles of PAAD patients and the corresponding clinical characteristics data of PAAD patients were downloaded from the public database. The least absolute shrinkage and selection operator (LASSO) Cox analysis model was used to identify and construct the prognostic gene signature in The Cancer Genome Atlas (TCGA) cohort. The PAAD patients used for verification are from the International Cancer Genome Consortium (ICGC) cohort. The Kaplan–Meier method was used to compare the overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were performed to identify the independent predictors of OS. Gene set enrichment analysis (GSEA) was performed to obtain gene ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the correlation between gene expression and immune infiltrates was investigated via single sample gene set enrichment analysis (ssGSEA). The GEPIA database was performed to examine prognostic genes in PAAD. LASSO Cox regression analysis was used to construct a model of inflammatory response-related gene signature. Compared with the low-risk group, patients in the high-risk group had significantly lower OS. The receiver operating characteristic curve (ROC) analysis confirmed the signature’s predictive capacity. Multivariate Cox analysis showed that risk score is an independent predictor of OS. Functional analysis shows that the immune status between the two risk groups is significantly different, and the cancer-related pathways were abundant in the high-risk group. Moreover, the risk score is significantly related to tumor grade, stage, and immune infiltration types. It was also obtained that the expression level of prognostic genes was significantly correlated with the sensitivity of cancer cells to anti-tumor drugs. In addition, there are significant differences in the expression of PAAD tissues and adjacent non-tumor tissues. The novel signature constructed from five inflammatory response-related genes can be used to predict prognosis and affect the immune status of PAAD. In addition, suppressing these genes may be a treatment option.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

DATA AVAILABILITY

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Abbreviations

PAAD:

Pancreatic adenocarcinoma

TCGA:

The Cancer Genome Atlas

ICGC:

International Cancer Genome Consortium

DEGs:

Differentially expressed genes

BH:

Benjamini–Hochberg

PCA:

Principal component analysis

ROC:

Receiver operating characteristic

KEGG:

Kyoto Encyclopedia of Genes and Genomes

GSEA:

Gene set enrichment analysis

GO:

Gene ontology

ssGSEA:

Single-sample gene set enrichment analysis

ANOVA:

Analysis of variance

NCI:

National Cancer Institute

AUC:

Area under the curve

aDCs:

A dendritic cells

Treg:

Regulatory T cells

APC:

Antigen-presenting cells

MHC:

Major histocompatibility complex

HLA:

Human leukocyte antigen

CCR:

Carbon catabolite repression

TME:

Tumor microenvironment

mDNAsi:

Stemness index

DNAss:

DNA stemness score

RNAss:

RNA stemness score

IFN-γ:

Interferon-γ

References

  1. Xu, T., X. Xu, P.C. Liu, H. Mao, and S. Ju. 2020. Transcriptomic analyses and potential therapeutic targets of pancreatic cancer with concomitant diabetes. Frontiers in Oncology 10: 563527. https://doi.org/10.3389/fonc.2020.563527.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Li, B., C. Zhang, J. Wang, M. Zhang, C. Liu, and Z. Chen. 2020. Impact of genetic variants of ABCB1, APOB, CAV1, and NAMPT on susceptibility to pancreatic ductal adenocarcinoma in Chinese patients. Molecular Genetics & Genomic Medicine 8: e1226. https://doi.org/10.1002/mgg3.1226.

    Article  CAS  Google Scholar 

  3. Chen, J.X., C.S. Cheng, H.F. Gao, Z.J. Chen, L.L. Lv, J.Y. Xu, X.H. Shen, J. Xie, and L. Zheng. 2021. Overexpression of interferon-inducible protein 16 promotes progression of human pancreatic adenocarcinoma through interleukin-1β-induced tumor-associated macrophage infiltration in the tumor microenvironment. Frontiers in Cell and Developmental Biology 9:640786. https://doi.org/10.3389/fcell.2021.640786.

  4. Okamoto, H., H. Kikuchi, H. Naganuma, and T. Kamei. 2020. Multiple carcinosarcomas of the esophagus with adeno-carcinomatous components: A case report. World Journal of Gastroenterology 26: 2111–2118. https://doi.org/10.3748/wjg.v26.i17.2111.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Setrerrahmane, S., and H. Xu. 2017. Tumor-related interleukins: Old validated targets for new anti-cancer drug development. Molecular Cancer 16: 153. https://doi.org/10.1186/s12943-017-0721-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Balkwill, F., and A. Mantovani. 2001. Inflammation and cancer: Back to Virchow? Lancet 357: 539–545. https://doi.org/10.1016/S0140-6736(00)04046-0.

    Article  CAS  PubMed  Google Scholar 

  7. Maruyama, T., T. Tomofuji, T. Machida, H. Kato, K. Tsutsumi, D. Uchida, A. Takaki, T. Yoneda, H. Miyai, H. Mizuno, D. Ekuni, H. Okada, and M. Morita. 2017. Association between periodontitis and prognosis of pancreatobiliary tract cancer: A pilot study. Molecular and Clinical Oncology 6: 683–687. https://doi.org/10.3892/mco.2017.1220.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hurley, D.G., D.M. Budden, and E.J. Crampin. 2015. Virtual reference environments: A simple way to make research reproducible. Briefings in Bioinformatics 16: 901–903. https://doi.org/10.1093/bib/bbu043.

    Article  PubMed  Google Scholar 

  9. Benjamini, Y., and Y. Hochberg. 1995. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological). 57: 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

    Article  Google Scholar 

  10. Sun, H., and S. Wang. 2012. Penalized logistic regression for high-dimensional DNA methylation data with case-control studies. Bioinformatics 28: 1368–1375. https://doi.org/10.1093/bioinformatics/bts145.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Flobak, Å., B. Niederdorfer, V.T. Nakstad, L. Thommesen, G. Klinkenberg, and A. Lægreid. 2019. A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines. Scientific Data 6: 237. https://doi.org/10.1038/s41597-019-0255-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ji, X., F. Ding, J. Gao, X. Huang, W. Liu, Y. Wang, Q. Liu, and T. Xin. 2020. Molecular and clinical characterization of a novel prognostic and immunologic biomarker FAM111A in diffuse lower-grade glioma. Frontiers in Oncology 10: 573800. https://doi.org/10.3389/fonc.2020.573800.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Chao, B., X. Ju, L. Zhang, X. Xu, and Y. Zhao. 2020. A novel prognostic marker systemic inflammation response index (SIRI) for operable cervical cancer patients. Frontiers in Oncology 10: 766. https://doi.org/10.3389/fonc.2020.00766.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hsu, B.E., Y. Shen, and P.M. Siegel. 2020. Neutrophils: Orchestrators of the malignant phenotype. Frontiers in Immunology 11: 1778. https://doi.org/10.3389/fimmu.2020.01778.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Yang, C.C., L.D. Hsiao, and C.M. Yang. 2020. Galangin inhibits LPS-induced MMP-9 expression via suppressing protein kinase-dependent AP-1 and FoxO1 activation in rat brain astrocytes. Journal of Inflammation Research 13: 945–960. https://doi.org/10.2147/JIR.S276925.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hoffmann, T.K., G. Dworacki, T. Tsukihiro, N. Meidenbauer, W. Gooding, J.T. Johnson, and T.L. Whiteside. 2002. Spontaneous apoptosis of circulating T lymphocytes in patients with head and neck cancer and its clinical importance. Clinical Cancer Research 8: 2553–2562.

    PubMed  Google Scholar 

  17. Iwai, N., T. Okuda, J. Sakagami, T. Harada, T. Ohara, M. Taniguchi, H. Sakai, K. Oka, T. Hara, T. Tsuji, T. Komaki, K. Kagawa, H. Yasuda, Y. Naito, and Y. Itoh. 2020. Neutrophil to lymphocyte ratio predicts prognosis in unresectable pancreatic cancer. Science and Reports 10: 18758. https://doi.org/10.1038/s41598-020-75745-8.

    Article  CAS  Google Scholar 

  18. Liu, Z., W. Liang, D. Kang, Q. Chen, Z. Ouyang, H. Yan, B. Huang, D. Jin, Y. Chen, and Q. Li. 2020. Increased osteoblastic Cxcl9 contributes to the uncoupled bone formation and resorption in postmenopausal osteoporosis. Clinical Interventions in Aging 15: 1201–1212. https://doi.org/10.2147/CIA.S254885.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wang, Y., X. Wang, X. Huang, J. Zhang, J. Hu, Y. Qi, B. Xiang, and Q. Wang. 2021. Integrated genomic and transcriptomic analysis reveals key genes for predicting dual-phenotype hepatocellular carcinoma prognosis. Journal of Cancer 12: 2993–3010. https://doi.org/10.7150/jca.56005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Pein, M., J. Insua-Rodríguez, T. Hongu, A. Riedel, J. Meier, L. Wiedmann, K. Decker, M.A.G. Essers, H.P. Sinn, S. Spaich, M. Sütterlin, A. Schneeweiss, A. Trumpp, and T. Oskarsson. 2020. Metastasis-initiating cells induce and exploit a fibroblast niche to fuel malignant colonization of the lungs. Nature Communications 11: 1494. https://doi.org/10.1038/s41467-020-15188-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kuo, P., Z.K. Tuong, S.M. Teoh, I.H. Frazer, S.R. Mattarollo, and G.R. Leggatt. 2018. HPV16E7-induced hyperplasia promotes CXCL9/10 expression and induces CXCR3+ T-cell migration to skin. The Journal of Investigative Dermatology 138: 1348–1359. https://doi.org/10.1016/j.jid.2017.12.021.

    Article  CAS  PubMed  Google Scholar 

  22. Matsumoto, M., H. Oshiumi, and T. Seya. 2011. Antiviral responses induced by the TLR3 pathway. Reviews in Medical Virology 21: 67–77. https://doi.org/10.1002/rmv.680.

    Article  CAS  PubMed  Google Scholar 

  23. Meng, Q., and B. Qiu. 2020. Exosomal MicroRNA-320a derived from mesenchymal stem cells regulates rheumatoid arthritis fibroblast-like synoviocyte activation by suppressing CXCL9 expression. Frontiers in Physiology 11: 441. https://doi.org/10.3389/fphys.2020.00441.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Kotrych, D., V. Dziedziejko, K. Safranow, and A. Pawlik. 2015. Lack of association between CXCL9 and CXCL10 gene polymorphisms and the outcome of rheumatoid arthritis treatment with methotrexate. European Review for Medical and Pharmacological Sciences 19: 3037–3040.

    CAS  PubMed  Google Scholar 

  25. Hasegawa, T., V. Venkata Suresh, Y. Yahata, M. Nakano, S. Suzuki, S. Suzuki, S. Yamada, H. Kitaura, I. Mizoguchi, Y. Noiri, K. Handa, and M. Saito. 2021. Inhibition of the CXCL9-CXCR3 axis suppresses the progression of experimental apical periodontitis by blocking macrophage migration and activation. Science and Reports 11: 2613. https://doi.org/10.1038/s41598-021-82167-7.

    Article  CAS  Google Scholar 

  26. Corrales, L., V. Matson, B. Flood, S. Spranger, and T.F. Gajewski. 2017. Innate immune signaling and regulation in cancer immunotherapy. Cell Research 27: 96–108. https://doi.org/10.1038/cr.2016.149.

    Article  CAS  PubMed  Google Scholar 

  27. Aguilera-Durán, G., and A. Romo-Mancillas. 2020. Computational study of C-X-C chemokine receptor (CXCR)3 binding with its natural agonists chemokine (C-X-C Motif) ligand (CXCL) 9, 10 and 11 and with synthetic antagonists: Insights of receptor activation towards drug design for vitiligo. Molecules 25: 4413. https://doi.org/10.3390/molecules25194413.

    Article  CAS  PubMed Central  Google Scholar 

  28. Akkari, L., V. Gocheva, J.C. Kester, K.E. Hunter, M.L. Quick, L. Sevenich, H.W. Wang, C. Peters, L.H. Tang, D.S. Klimstra, T. Reinheckel, and J.A. Joyce. 2014. Distinct functions of macrophage-derived and cancer cell-derived cathepsin Z combine to promote tumor malignancy via interactions with the extracellular matrix. Genes & Development 28: 2134–2150. https://doi.org/10.1101/gad.249599.114.

    Article  CAS  Google Scholar 

  29. Li, X., M.J. Large, C.J. Creighton, R.B. Lanz, J.W. Jeong, S.L. Young, B.A. Lessey, W.A. Palomino, S.Y. Tsai, and F.J. Demayo. 2013. COUP-TFII regulates human endometrial stromal genes involved in inflammation. Molecular Endocrinology 27: 2041–2054. https://doi.org/10.1210/me.2013-1191.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chen, P.H., H. Yao, and L.J. Huang. 2017. Cytokine receptor endocytosis: New kinase activity-dependent and -independent roles of PI3K. Frontiers in Endocrinology (Lausanne) 8: 78. https://doi.org/10.3389/fendo.2017.00078.

    Article  Google Scholar 

  31. Maas, N.L., and J.A. Diehl. 2015. Molecular pathways: The PERKs and pitfalls of targeting the unfolded protein response in cancer. Clinical Cancer Research 21 (4): 675–679. https://doi.org/10.1158/1078-0432.CCR-13-3239.

    Article  CAS  PubMed  Google Scholar 

  32. Nagelkerke, A., H. Mujcic, J. Bussink, B.G. Wouters, H.W. van Laarhoven, F.C. Sweep, and P.N. Span. 2011. Hypoxic regulation and prognostic value of LAMP3 expression in breast cancer. Cancer 117: 3670–3681. https://doi.org/10.1002/cncr.25938.

    Article  CAS  PubMed  Google Scholar 

  33. Liu, S., J. Yue, W. Du, J. Han, and W. Zhang. 2018. LAMP3 plays an oncogenic role in osteosarcoma cells partially by inhibiting TP53. Cellular & Molecular Biology Letters 11 (23): 33. https://doi.org/10.1186/s11658-018-0099-8. PMID:30008754;PMCID:PMC6042264

    Article  CAS  Google Scholar 

  34. Liao, X., Y. Chen, D. Liu, F. Li, X. Li, and W. Jia. 2015. High expression of LAMP3 is a novel biomarker of poor prognosis in patients with esophageal squamous cell carcinoma. International Journal of Molecular Sciences 16: 17655–17667. https://doi.org/10.3390/ijms160817655.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kanao, H., T. Enomoto, T. Kimura, M. Fujita, R. Nakashima, Y. Ueda, Y. Ueno, T. Miyatake, T. Yoshizaki, G.S. Buzard, A. Tanigami, K. Yoshino, and Y. Murata. 2005. Overexpression of LAMP3/TSC403/DC-LAMP promotes metastasis in uterine cervical cancer. Cancer Research 65: 8640–8645. https://doi.org/10.1158/0008-5472.CAN-04-4112.

    Article  CAS  PubMed  Google Scholar 

  36. Liu, K., Q. He, G. Liao, and J. Han. 2015. Identification of critical genes and gene interaction networks that mediate osteosarcoma metastasis to the lungs. Experimental and Therapeutic Medicine 10: 1796–1806. https://doi.org/10.3892/etm.2015.2767.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kosti, A., P.R. de Araujo, W.Q. Li, G.D.A. Guardia, J. Chiou, C. Yi, D. Ray, F. Meliso, Y.M. Li, T. Delambre, M. Qiao, S.S. Burns, F.K. Lorbeer, F. Georgi, M. Flosbach, S. Klinnert, A. Jenseit, X. Lei, C.R. Sandoval, K. Ha, H. Zheng, R. Pandey, A. Gruslova, Y.K. Gupta, A. Brenner, E. Kokovay, T.R. Hughes, Q.D. Morris, P.A.F. Galante, S. Tiziani, and L.O.F. Penalva. 2020. The RNA-binding protein SERBP1 functions as a novel oncogenic factor in glioblastoma by bridging cancer metabolism and epigenetic regulation. Genome Biology 21: 195. https://doi.org/10.1186/s13059-020-02115-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Alessandrini, F., L. Pezzè, D. Menendez, M.A. Resnick, and Y. Ciribilli. 2018. ETV7-mediated DNAJC15 repression leads to doxorubicin resistance in breast cancer cells. Neoplasia 20: 857–870. https://doi.org/10.1016/j.neo.2018.06.008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wang, C., B. Qi, C. Zhang, and J. Cheng. 2017. Identification of key genes influenced by fixation stability in early fracture hematoma and elucidation of their roles in fracture healing. Experimental and Therapeutic Medicine 14: 4633–4638. https://doi.org/10.3892/etm.2017.5192.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hundsdorfer, B., H.F. Zeilhofer, K.P. Bock, P. Dettmar, M. Schmitt, and H.H. Horch. 2004. Prognostische Bedeutung des Plasminogenaktivators vom Urokinasetyp (uPA) und des Plasminogenaktivator-Inhibitors (PAI-1) beim primär resezierten Plattenepithelkarzinom der Mundhöhle [The prognostic importance of urinase type plasminogen activators (uPA) and plasminogen activator inhibitors (PAI-1) in the primary resection of oral squamous cell carcinoma]. Mund Kiefer Gesichtschir 8:173–179. German. https://doi.org/10.1007/s10006-003-0520-x.

  41. Annecke, K., M. Schmitt, U. Euler, M. Zerm, D. Paepke, S. Paepke, G. von Minckwitz, C. Thomssen, and N. Harbeck. 2008. uPA and PAI-1 in breast cancer: Review of their clinical utility and current validation in the prospective NNBC-3 trial. Advances in Clinical Chemistry 45: 31–45. https://doi.org/10.1016/s0065-2423(07)00002-9.

    Article  CAS  PubMed  Google Scholar 

  42. Harbeck, N., M. Schmitt, S. Paepke, H. Allgayer, and R.E. Kates. 2007. Tumor-associated proteolytic factors uPA and PAI-1: Critical appraisal of their clinical relevance in breast cancer and their integration into decision-support algorithms. Critical Reviews in Clinical Laboratory Sciences 44: 179–201. https://doi.org/10.1080/10408360601040970.

    Article  CAS  PubMed  Google Scholar 

  43. Cariello, M., E. Piccinin, R. Zerlotin, M. Piglionica, C. Peres, C. Divella, A. Signorile, G. Villani, G. Ingravallo, C. Sabbà, and A. Moschetta. 2021. Adhesion of platelets to colon cancer cells is necessary to promote tumor development in xenograft, genetic and inflammation models. Cancers (Basel) 13: 4243. https://doi.org/10.3390/cancers13164243.

    Article  CAS  Google Scholar 

  44. Koltsova, E.K., P. Sundd, A. Zarpellon, H. Ouyang, Z. Mikulski, A. Zampolli, Z.M. Ruggeri, and K. Ley. 2014. Genetic deletion of platelet glycoprotein Ib alpha but not its extracellular domain protects from atherosclerosis. Thrombosisa and Haemostasis 112(6):1252–63. https://doi.org/10.1160/TH14-02-0130. Epub 2014 Aug 7. PMID: 25104056; PMCID: PMC4429870.

  45. Song, Y., J.A. Aglipay, J.D. Bernstein, S. Goswami, and P. Stanley. 2010. The bisecting GlcNAc on N-glycans inhibits growth factor signaling and retards mammary tumor progression. Cancer Research 70 (8): 3361–3371. https://doi.org/10.1158/0008-5472. CAN-09-2719.PMID:20395209;PMCID:PMC2856092

  46. Zhang, X., W. Chen, N. Yin, L. Dong, M. Fu, Q. Zhan, and T. Tong. 2019. Regulation of OLC1 protein expression by the anaphase-promoting complex. Oncology Letters 17: 2639–2646. https://doi.org/10.3892/ol.2019.9881.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhang, E., and Y. Wu. 2014. Dual effects of miR-155 on macrophages at different stages of atherosclerosis: LDL is the key? Medical Hypotheses 83: 74–78. https://doi.org/10.1016/j.mehy.2014.04.004.

    Article  CAS  PubMed  Google Scholar 

  48. Horiguchi, S., H. Shiraha, T. Nagahara, J. Kataoka, M. Iwamuro, M. Matsubara, S. Nishina, H. Kato, A. Takaki, K. Nouso, T. Tanaka, K. Ichimura, T. Yagi, and K. Yamamoto. 2013. Loss of runt-related transcription factor 3 induces gemcitabine resistance in pancreatic cancer. Molecular Oncology 7: 840–849. https://doi.org/10.1016/j.molonc.2013.04.004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Tekin, C., H.L. Aberson, C. Waasdorp, G.K.J. Hooijer, O.J. de Boer, F. Dijk, M.F. Bijlsma, and C.A. Spek. 2020. Macrophage-secreted MMP9 induces mesenchymal transition in pancreatic cancer cells via PAR1 activation. Cellular Oncology (Dordrecht) 43: 1161–1174. https://doi.org/10.1007/s13402-020-00549-x.

    Article  CAS  Google Scholar 

  50. García-Sáenz, M., D. Uribe-Cortés, C. Ramírez-Rentería, and A. Ferreira-Hermosillo. 2018. Difficult-to-diagnose diabetes in a patient treated with cyclophosphamide - the contradictory roles of immunosuppressant agents: A case report. Journal of Medical Case Reports 12 (1): 364. https://doi.org/10.1186/s13256-018-1925-3. PMID:30526658;PMCID:PMC6287356.

  51. Gelsomino, L., G.D. Naimo, R. Malivindi, G. Augimeri, S. Panza, C. Giordano, I. Barone, D. Bonofiglio, L. Mauro, S. Catalano, and S. Andò. 2020. Knockdown of leptin receptor affects macrophage phenotype in the tumor microenvironment inhibiting breast cancer growth and progression. Cancers (Basel) 12: 2078. https://doi.org/10.3390/cancers12082078.

    Article  CAS  Google Scholar 

  52. Zhang, L., H. Liang, H. Guan, and H. Liu. 2015. Study of the association between CD28/CTLA-4 expression and disease activity in juvenile idiopathic arthritis. Experimental and Therapeutic Medicine 9: 1733–1738. https://doi.org/10.3892/etm.2015.2295.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Sabanathan, D., J.J. Park, M. Marquez, L. Francisco, N. Byrne, and H. Gurney. 2017. Cure in advanced renal cell cancer: Is it an achievable goal? The Oncologist 22: 1470–1477. https://doi.org/10.1634/theoncologist.2017-0159.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Smith, M.A., C.C. Chiang, K. Zerrouki, S. Rahman, W.I. White, K. Streicher, W.A. Rees, A. Schiffenbauer, L.G. Rider, F.W. Miller, Z. Manna, S. Hasni, M.J. Kaplan, R. Siegel, D. Sinibaldi, M.A. Sanjuan, and K.A. Casey. 2020. Using the circulating proteome to assess type I interferon activity in systemic lupus erythematosus. Science and Reports 10: 4462. https://doi.org/10.1038/s41598-020-60563-9.

    Article  CAS  Google Scholar 

  55. Brunel, A., G. Bégaud, C. Auger, S. Durand, S. Battu, B. Bessette, and M. Verdier. 2021. Autophagy and extracellular vesicles, connected to rabGTPase family, support aggressiveness in cancer stem cells. Cells 10: 1330. https://doi.org/10.3390/cells10061330.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Almahmoud, S., and H.A. Zhong. 2019. Molecular modeling studies on the binding mode of the PD-1/PD-L1 Complex Inhibitors. International Journal of Molecular Sciences 20: 4654. https://doi.org/10.3390/ijms20184654.

    Article  CAS  PubMed Central  Google Scholar 

Download references

ACKNOWLEDGEMENTS

Acknowledgments to the TCGA and ICGC databases for providing researchable patient data.

Funding

This study was supported by (1) Key Laboratory of Tumor Precision Medicine, Hunan Colleges and Universities Project (2019–379), (2) a project supported by Scientific Research Fund of Hunan Provincial Education Department (19a458), and (3) Scientific Research Project of Hunan Provincial Health Commission (20201718).

Author information

Author notes

  1. Zu-Liang Deng, Ding-Zhong Zhou, and Su-Juan Cao contributed equally to this work.

Authors and Affiliations

  1. Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People’s Republic of China

    Zu-Liang Deng & Hui Xie

  2. Department of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People’s Republic of China

    Ding-Zhong Zhou & Qing Li

  3. Department of Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People’s Republic of China

    Su-Juan Cao

  4. Department of Physical Examination, Beihu Centers for Disease Control and Prevention, Chenzhou, 423000, People’s Republic of China

    Jian-Fang Zhang

Authors
  1. Zu-Liang Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ding-Zhong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Su-Juan Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jian-Fang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hui Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Zu-Liang Deng, Ding-Zhong Zhou, Su-Juan Cao, and Hui Xie: substantial contributions to the conception and design of the work; Zu-Liang Deng, Ding-Zhong Zhou, Su-Juan Cao, Qing Li, Jian-Fang Zhang, and Hui Xie: the acquisition, analysis, and interpretation of data for the work; Zu-Liang Deng, Ding-Zhong Zhou, and Su-Juan Cao: drafting the work; Hui Xie: revising it critically for important intellectual content; Zu-Liang Deng, Ding-Zhong Zhou, Su-Juan Cao, Qing Li, Jian-Fang Zhang, and Hui Xie: final approval of the version to be published; Zu-Liang Deng, Ding-Zhong Zhou, Su-Juan Cao, Qing Li, Jian-Fang Zhang, and Hui Xie: agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to Hui Xie.

Ethics declarations

Ethics Approval and Consent to Participate

N/A.

Consent for Publication

N/A.

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, ZL., Zhou, DZ., Cao, SJ. et al. Development and Validation of an Inflammatory Response-Related Gene Signature for Predicting the Prognosis of Pancreatic Adenocarcinoma. Inflammation 45, 1732–1751 (2022). https://doi.org/10.1007/s10753-022-01657-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10753-022-01657-6

KEY WORDS

Search

Navigation