Abstract
Background: Glioma is the most common malignant intracranial tumor with high lethality. Despite surgery combined with chemoradiotherapy, the prognosis for patients with glioma remains poor. This is primarily due to acquired chemoradiotherapy resistance. Therefore, to improve the prognosis of glioma, further study into the mechanism of chemoradiotherapy resistance is needed.
Objective: This study aimed to (1) evaluate the prognosis of patients with glioma by using a prognostic risk score model constructed by chemoradiotherapy resistance genes, (2) provide new targets and directions for precise treatment of glioma, and (3) discuss the tumor heterogeneity of tumor cells.
Methods: According to therapy class and overall survival (OS), we identified 53 genes associated with glioma chemoradiotherapy resistance in The Cancer Genome Atlas Glioblastoma (TCGA GBM) database. Considering the important role of chemoradiotherapy resistance-related genes in the prognosis of glioma, we preliminarily screened and identified vital prognostic factors among these genes by using the Cox regression model of absolute contraction and selection operators in the TCGA GBM lower-grade glioma (TCGA GBMLGG) dataset. Next, the heterogeneity of the chemoradiotherapy resistance-associated genes in different glioma cells was revealed by single-cell sequencing in the GSE117891 cohort.
Results: A prognostic risk score model consisting of three genes (ARL4C, MSN, TNFAIP6) was constructed. The expression of this model was high in glioma neural progenitor cells (NPCs) and low in glioma oligodendrocytes. The OS rates were significantly lower in the high- vs. low-risk group.
Conclusion: Our 3 gene risk score complements the current glioma diagnosis and provides a novel insight into chemoradiotherapy resistance mechanisms for the prognosis of patients with glioma.
Keywords: Glioma, chemoradiotherapy resistance, neural progenitor cells, prognostic power, TCGA, tissue microarray, single-cell sequencing.
[http://dx.doi.org/10.1007/s00401-016-1545-1] [PMID: 27157931]
[http://dx.doi.org/10.1038/nrdp.2015.17] [PMID: 27188790]
[http://dx.doi.org/10.1038/nrclinonc.2016.204] [PMID: 28031556]
[http://dx.doi.org/10.1007/s12094-017-1631-4] [PMID: 28255650]
[http://dx.doi.org/10.1016/S0140-6736(18)30990-5] [PMID: 30060998]
[http://dx.doi.org/10.1002/14651858.CD013261.pub2] [PMID: 32202316]
[http://dx.doi.org/10.1200/JCO.2009.23.6497] [PMID: 19901110]
[http://dx.doi.org/10.1093/neuonc/now133] [PMID: 27370396]
[http://dx.doi.org/10.1056/NEJMoa043330] [PMID: 15758009]
[http://dx.doi.org/10.1001/jama.2013.280319] [PMID: 24193082]
[http://dx.doi.org/10.3171/2018.4.JNS18424] [PMID: 30485243]
[http://dx.doi.org/10.1016/j.ccell.2018.03.020] [PMID: 29681511]
[http://dx.doi.org/10.1124/pr.117.014944] [PMID: 29669750]
[http://dx.doi.org/10.1126/science.1254257] [PMID: 24925914]
[http://dx.doi.org/10.1016/j.ccell.2020.04.001] [PMID: 32396858]
[http://dx.doi.org/10.1101/gad.324301.119] [PMID: 31160393]
[http://dx.doi.org/10.1158/0008-5472.CAN-09-2307] [PMID: 19920198]
[http://dx.doi.org/10.1093/neuonc/now247] [PMID: 28031383]
[http://dx.doi.org/10.1016/j.jtho.2019.06.009] [PMID: 31228623]
[http://dx.doi.org/10.1016/j.eururo.2019.06.030] [PMID: 31345636]
[http://dx.doi.org/10.1038/s41422-019-0195-y] [PMID: 31273297]
[http://dx.doi.org/10.1126/science.aai8478] [PMID: 28360267]
[http://dx.doi.org/10.1093/neuonc/nou087] [PMID: 24842956]
[http://dx.doi.org/10.1016/S1470-2045(17)30345-5] [PMID: 28593859]
[PMID: 10914698]
[http://dx.doi.org/10.1186/s13046-020-01778-6] [PMID: 33228738]
[http://dx.doi.org/10.1039/D1NH00182E] [PMID: 34110340]
[http://dx.doi.org/10.1016/j.critrevonc.2021.103508] [PMID: 34678323]
[PMID: 20428813]
[http://dx.doi.org/10.1038/s41467-019-11719-3] [PMID: 31462642]
[http://dx.doi.org/10.1016/j.biomaterials.2021.120784] [PMID: 33848731]
[http://dx.doi.org/10.1038/nature05236] [PMID: 17051156]
[http://dx.doi.org/10.1038/s41467-020-17717-0] [PMID: 32753598]
[http://dx.doi.org/10.1038/nature11287] [PMID: 22854781]
[http://dx.doi.org/10.1158/0008-5472.CAN-07-1045] [PMID: 17908999]
[http://dx.doi.org/10.1016/j.cell.2017.07.016] [PMID: 28823557]
[http://dx.doi.org/10.1016/j.brainres.2019.146422] [PMID: 31472111]
[http://dx.doi.org/10.7150/jca.45052] [PMID: 33403039]