Zhou J.a· Guo Y.b· Fu J.a· Chen Q.a

Author affiliations

aDepartment of Neurosurgery, Tonglu First People’s Hospital, Tonglu, China
bDepartment of Cardiothoracic and Vascular Surgery, Tonglu First People’s Hospital, Tonglu, China

Log in to MyKarger to check if you already have access to this content.

Buy FullText & PDF Unlimited re-access via MyKarger Unrestricted printing, no saving restrictions for personal use read more

CHF 38.00 *
EUR 35.00 *
USD 39.00 *

Select

KAB

Buy a Karger Article Bundle (KAB) and profit from a discount!

If you would like to redeem your KAB credit, please log in.

Save over 20% compared to the individual article price.

Learn more

Rent/Cloud Rent for 48h to view Buy Cloud Access for unlimited viewing via different devices Synchronizing in the ReadCube Cloud Printing and saving restrictions apply Rental: USD 8.50
Cloud: USD 20.00

Select

Subscribe Access to all articles of the subscribed year(s) guaranteed for 5 years Unlimited re-access via Subscriber Login or MyKarger Unrestricted printing, no saving restrictions for personal use read more

Subcription rates

Select

* The final prices may differ from the prices shown due to specifics of VAT rules.

Article / Publication Details

First-Page Preview

Received: November 02, 2021
Accepted: February 09, 2022
Published online: March 30, 2022

Number of Print Pages: 12
Number of Figures: 8
Number of Tables: 0

ISSN: 1021-7401 (Print)
eISSN: 1423-0216 (Online)

For additional information: https://www.karger.com/NIM

Abstract

Objective: This study aims to construct a prognostic model based on the different immune infiltration statuses of the glioma samples. Methods: Glioma-associated dataset was assessed from The Cancer Genome Atlas database. Hierarchical cluster analysis was performed to classify the glioma samples. Single-sample gene set enrichment analysis was introduced to the glioma samples for immune infiltration analysis. Kaplan-Meier survival analysis was applied to evaluate patients’ prognoses. The differentially expressed genes (DEGs) between different sample groups were screened using limma package. Univariate Cox, LASSO Cox, and multivariate Cox regression analyses were employed to construct the prognostic model. The prediction performance of the model was examined by plotting a receiver-operating characteristic (ROC) curve, and GSEA was introduced to screen the differently activated pathways between high- and low-risk groups. Results: The glioma samples were classified into 3 clusters where the different immune infiltration and survival statuses were presented among the clusters. 123 immune-related DEGs were screened from the differential expression analyses, and based on these DEGs, an 8-gene prognostic model was constructed. The ROC curve exhibited an optimal performance of the prognostic model, and GSEA showed that ECM-receptor interaction, complement and coagulation cascades, cytokine receptor pathways, and viral protein interaction with cytokine were differently activated between the two risk groups. Conclusion: The current study screened an immune-associated gene set by classifying and differential analysis, followed by constructing an 8-gene prognostic model based on the screened genes.

© 2022 S. Karger AG, Basel

References Ostrom QT, Cioffi G, Gittleman H, Patil N, Waite K, Kruchko C, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012–2016. Neuro Oncol. 2019;21:v1–100. Cancer Genome Atlas Research Network; Brat DJ, Verhaak RG, Aldape KD, Yung WK, Salama SR, et al. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med. 2015;372:2481–98. Ostrom QT, Gittleman H, Fulop J, Liu M, Blanda R, Kromer C, et al. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro Oncol. 2015;17(Suppl 4):iv1–62. Xu S, Tang L, Dai G, Luo C, Liu Z. Immune-related genes with APA in microenvironment indicate risk stratification and clinical prognosis in grade II/III gliomas. Mol Ther Nucleic Acids. 2021;23:1229–42. Bleeker FE, Molenaar RJ, Leenstra S. Recent advances in the molecular understanding of glioblastoma. J Neurooncol. 2012;108:11–27. Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018;24:541–50. Deng X, Lin D, Zhang X, Shen X, Yang Z, Yang L, et al. Profiles of immune-related genes and immune cell infiltration in the tumor microenvironment of diffuse lower-grade gliomas. J Cell Physiol. 2020;235:7321–31. Jiang H, Xu S, Chen C. A ten-gene signature-based risk assessment model predicts the prognosis of lung adenocarcinoma. BMC Cancer. 2020;20:782. Zhang BH, Yang J, Jiang L, Lyu T, Kong LX, Tan YF, et al. Development and validation of a 14-gene signature for prognosis prediction in hepatocellular carcinoma. Genomics. 2020;112:2763–71. Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462:108–12. Witten DM, Tibshirani R. A framework for feature selection in clustering. J Am Stat Assoc. 2010;105:713–26. Kumar M, Sonker PK, Saroj A, Jain A, Bhattacharjee A, Saroj RK. Parametric survival analysis using R: illustration with lung cancer data. Cancer Rep. 2020;3:e1210. Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol Biol. 2018;1711:243–59. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47. Yu G, Wang LG, Han Y, He QY. ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7. Therneau TM, Grambsch PM. Modeling survival data: extending the cox model. New York: Springer-Verlag; 2000. Engebretsen S, Bohlin J. Statistical predictions with glmnet. Clin Epigenetics. 2019;11:123. Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013;32:5381–97. Klemm F, Maas RR, Bowman RL, Kornete M, Soukup K, Nassiri S, et al. Interrogation of the microenvironmental landscape in brain tumors reveals disease-specific alterations of immune cells. Cell. 2020;181:1643–60. Padoan A, Plebani M, Basso D. Inflammation and pancreatic cancer: focus on metabolism, cytokines, and immunity. Int J Mol Sci. 2019;20:676. Zhang L, Xu Y, Sun J, Chen W, Zhao L, Ma C, et al. M2-like tumor-associated macrophages drive vasculogenic mimicry through amplification of IL-6 expression in glioma cells. Oncotarget. 2017;8:819–32. Liu X, Huang H, Li X, Zheng X, Zhou C, Xue B, et al. Knockdown of ADAMDEC1 inhibits the progression of glioma in vitro. Histol Histopathol. 2020;35:997–1005. Jimenez-Pascual A, Lathia JD, Siebzehnrubl FA. ADAMDEC1 and FGF2/FGFR1 signaling constitute a positive feedback loop to maintain GBM cancer stem cells. Mol Cell Oncol. 2020;7:1684787. Zhang J, Liu Y, Guo Y, Zhao Q. GPX8 promotes migration and invasion by regulating epithelial characteristics in non-small cell lung cancer. Thorac Cancer. 2020;11:3299–308. Khatib A, Solaimuthu B, Ben Yosef M, Abu Rmaileh A, Tanna M, Oren G, et al. The glutathione peroxidase 8 (GPX8)/IL-6/STAT3 axis is essential in maintaining an aggressive breast cancer phenotype. Proc Natl Acad Sci U S A. 2020;117:21420–31. Barman A, Assmann A, Richter S, Soch J, Schütze H, Wüstenberg T, et al. Genetic variation of the RASGRF1 regulatory region affects human hippocampus-dependent memory. Front Hum Neurosci. 2014;8:260. Wu J, Sheng C, Liu Z, Jia W, Wang B, Li M, et al. Lmx1a enhances the effect of iNSCs in a PD model. Stem Cell Res. 2015;14:1–9. Lee QY, Mall M, Chanda S, Zhou B, Sharma KS, Schaukowitch K, et al. Pro-neuronal activity of Myod1 due to promiscuous binding to neuronal genes. Nat Cell Biol. 2020;22:401–11. Article / Publication Details

First-Page Preview

Received: November 02, 2021
Accepted: February 09, 2022
Published online: March 30, 2022

Number of Print Pages: 12
Number of Figures: 8
Number of Tables: 0

ISSN: 1021-7401 (Print)
eISSN: 1423-0216 (Online)

For additional information: https://www.karger.com/NIM

Copyright / Drug Dosage / Disclaimer Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.