Abstract
Progression, prognosis, and therapeutic strategy of stomach adenocarcinoma (STAD) have a close connection with tumor microenvironment (TME). Thus, it is pivotal to delve into the TME and immune-related genes, which may bring possibilities for improving patient’s prognosis. TCGA-STAD dataset was analyzed to acquire differentially expressed lncRNAs in tumor samples, which were overlapped with the immune-related lncRNA datasets in the ImmLnc database. Twenty-six lncRNAs related to STAD immunity and patient’s prognosis were acquired by univariate Cox analysis. Following lncRNA expression patterns, STAD samples could be classified into two clusters with completely different immune patterns. We performed multivariate Cox regression analysis on lncRNAs to identify 7-feature lncRNAs and constructed a corresponding prognostic model. The model validity was verified by survival analysis and ROC curve in validation and training sets. To explore connection between model and TME and tumor drug resistance, this study analyzed differences in immune cell infiltration between samples from high- and low-risk groups and then revealed immune cells follicular helper with significant differences in tumor tissue infiltration. Analysis of resistance to chemotherapeutic drugs revealed that samples in the high-risk group had resistance to cisplatin, doxorubicin, bleomycin, and gemcitabine. Through univariate and multivariate Cox analyses, we manifested that risk score could be an independent prognostic factor. Combining risk score and clinical factors, a nomogram was constructed to accurately predict patient’s prognosis. This model can effectively predict prognosis, TME, and drug resistance of STAD patients, which may provide a reference for tumor development evaluation and precise treatment for clinical STAD.
Graphical abstract
Similar content being viewed by others
Data Availability
The data used to support the findings of this study are included within the article. The data and materials in the current study are available from the corresponding author on reasonable request.
References
Balakrishnan, M., George, R., Sharma, A., & Graham, D. Y. (2017). Changing trends in stomach cancer throughout the world. Current gastroenterology reports, 19, 36. https://doi.org/10.1007/s11894-017-0575-8
Hoshi, H. (2020). Management of Gastric Adenocarcinoma for general surgeons. Surgical Clinics of North America, 100, 523–534. https://doi.org/10.1016/j.suc.2020.02.004
Zhang, M., et al. (2021). Dissecting transcriptional heterogeneity in primary gastric adenocarcinoma by single cell RNA sequencing. Gut, 70, 464–475. https://doi.org/10.1136/gutjnl-2019-320368
Steven, A., Fisher, S. A., & Robinson, B. W. (2016). Immunotherapy for lung cancer. Respirology, 21, 821–833. https://doi.org/10.1111/resp.12789
Sugie, T. (2018). Immunotherapy for metastatic breast cancer. Chin Clin Oncol, 7, 28. https://doi.org/10.21037/cco.2018.05.05
Morrison, A. H., Byrne, K. T., & Vonderheide, R. H. (2018). Immunotherapy and prevention of pancreatic cancer. Trends Cancer, 4, 418–428. https://doi.org/10.1016/j.trecan.2018.04.001
Lei, X., et al. (2020). Immune cells within the tumor microenvironment: Biological functions and roles in cancer immunotherapy. Cancer Letters, 470, 126–133. https://doi.org/10.1016/j.canlet.2019.11.009
Zhang, H., et al. (2015). Infiltration of diametrically polarized macrophages predicts overall survival of patients with gastric cancer after surgical resection. Gastric Cancer, 18, 740–750. https://doi.org/10.1007/s10120-014-0422-7
Hinshaw, D. C., & Shevde, L. A. (2019). The tumor microenvironment innately modulates cancer progression. Cancer Research, 79, 4557–4566. https://doi.org/10.1158/0008-5472.CAN-18-3962
Ren, N., Liang, B. & Li, Y. (2020). Identification of prognosis-related genes in the tumor microenvironment of stomach adenocarcinoma by TCGA and GEO datasets. Biosci Rep 40, 10.1042/BSR20200980.
Wu, M. et al. (2020). Development and validation of an immune-related gene prognostic model for stomach adenocarcinoma. Biosci Rep 40, 10.1042/BSR20201012.
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139–140. https://doi.org/10.1093/bioinformatics/btp616
Wilkerson, M. D., & Hayes, D. N. (2010). ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking. Bioinformatics, 26, 1572–1573. https://doi.org/10.1093/bioinformatics/btq170
Blanche, P., Dartigues, J. F., & Jacqmin-Gadda, H. (2013). Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Statistics in Medicine, 32, 5381–5397. https://doi.org/10.1002/sim.5958
Hu, X., Wu, L., Liu, B., & Chen, K. (2021). Immune infiltration subtypes characterization and identification of prognosis-related lncrnas in adenocarcinoma of the esophagogastric junction. Frontiers in Immunology, 12, 651056. https://doi.org/10.3389/fimmu.2021.651056
Huang, C., et al. (2019). Clinical significance of serum CA125, CA19-9, CA72-4, and fibrinogen-to-lymphocyte ratio in gastric cancer with peritoneal dissemination. Frontiers in Oncology, 9, 1159. https://doi.org/10.3389/fonc.2019.01159
Cancer Genome Atlas Research, N. (2014). Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202–209. https://doi.org/10.1038/nature13480.
Cristescu, R., et al. (2015). Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nature Medicine, 21, 449–456. https://doi.org/10.1038/nm.3850
Yoshida, N., et al. (2016). A High RORgammaT/CD3 Ratio is a strong prognostic factor for postoperative survival in advanced colorectal cancer: Analysis of helper T cell lymphocytes (Th1, Th2, Th17 and regulatory T cells). Annals of Surgical Oncology, 23, 919–927. https://doi.org/10.1245/s10434-015-4923-3
Tosolini, M., et al. (2011). Clinical impact of different classes of infiltrating T cytotoxic and helper cells (Th1, th2, treg, th17) in patients with colorectal cancer. Cancer Research, 71, 1263–1271. https://doi.org/10.1158/0008-5472.CAN-10-2907
Morin, P. J. (2019). Colorectal cancer: The APC-lncRNA link. The Journal of clinical investigation, 129, 503–505. https://doi.org/10.1172/JCI125985
Wang, L., et al. (2015). Targeting Cdc20 as a novel cancer therapeutic strategy. Pharmacology & Therapeutics, 151, 141–151. https://doi.org/10.1016/j.pharmthera.2015.04.002
Yang, X. Z., et al. (2018). LINC01133 as ceRNA inhibits gastric cancer progression by sponging miR-106a-3p to regulate APC expression and the Wnt/beta-catenin pathway. Molecular Cancer, 17, 126. https://doi.org/10.1186/s12943-018-0874-1
Kong, F., et al. (2018). Correction: ZFPM2-AS1, a novel lncRNA, attenuates the p53 pathway and promotes gastric carcinogenesis by stabilizing MIF. Oncogene, 37, 6010. https://doi.org/10.1038/s41388-018-0412-z
Li, J., et al. (2021). Hypoxic glioma stem cell-derived exosomes containing Linc01060 promote progression of glioma by regulating the MZF1/c-Myc/HIF1alpha axis. Cancer Research, 81, 114–128. https://doi.org/10.1158/0008-5472.CAN-20-2270
Gong, W. et al. (2019). Analysis of survival-related lncRNA landscape identifies a role for LINC01537 in energy metabolism and lung cancer progression. Int J Mol Sci 20, https://doi.org/10.3390/ijms20153713.
Wu, H., et al. (2021). Survival-related lncRNA landscape analysis identifies LINC01614 as an oncogenic lncRNA in gastric cancer. Frontiers in Genetics, 12, 698947. https://doi.org/10.3389/fgene.2021.698947
Campbell, G. R., & Spector, S. A. (2019). DIABLO/SMAC mimetics selectively kill HIV-1-infected resting memory CD4(+) T cells: A potential role in a cure strategy for HIV-1 infection. Autophagy, 15, 744–746. https://doi.org/10.1080/15548627.2019.1569950
Ino, Y., et al. (2013). Immune cell infiltration as an indicator of the immune microenvironment of pancreatic cancer. British Journal of Cancer, 108, 914–923. https://doi.org/10.1038/bjc.2013.32
Vinuesa, C. G., Linterman, M. A., Yu, D., & MacLennan, I. C. (2016). Follicular helper T cells. Annual Review of Immunology, 34, 335–368. https://doi.org/10.1146/annurev-immunol-041015-055605
Wen, Y., et al. (2020). TNF-alpha in T lymphocytes attenuates renal injury and fibrosis during nephrotoxic nephritis. American Journal of Physiology. Renal Physiology, 318, F107–F116. https://doi.org/10.1152/ajprenal.00347.2019
Han, Y., et al. (2019). IL-38 Ameliorates skin inflammation and limits IL-17 Production from gammadelta T Cells. Cell Rep, 27(835–846), 835. https://doi.org/10.1016/j.celrep.2019.03.082
Wu, T., & Dai, Y. (2017). Tumor microenvironment and therapeutic response. Cancer Letters, 387, 61–68. https://doi.org/10.1016/j.canlet.2016.01.043
Author information
Authors and Affiliations
Contributions
Dr. CBX contributed to the study conception and design. Dr. ZWC and Dr. XMP contributed to the material preparation, data collection, and analysis. Dr. ML and Dr. GXC draft the manuscript. Dr. JXL and Dr. YJM revised the manuscript. Then Dr. YJM gave the final approval of the version to be submitted. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics Approval and Consent to Participate
It does not contain any studies with human or animal subjects.
Consent for Publication
Not applicable.
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
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Xu, C., Chen, Z., Pan, X. et al. Construction of a Prognostic Evaluation Model for Stomach Adenocarcinoma on the Basis of Immune-Related lncRNAs. Appl Biochem Biotechnol 194, 6255–6269 (2022). https://doi.org/10.1007/s12010-022-04098-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12010-022-04098-x