Identification of Three Core Secretome Genes Associated with Immune Infiltration in High Tumor Mutation Burden Across 14 Major Solid Tumors.
immune infiltration
prognosis
secretome genes
solid tumors
tumor mutation burden
Journal
International journal of general medicine
ISSN: 1178-7074
Titre abrégé: Int J Gen Med
Pays: New Zealand
ID NLM: 101515487
Informations de publication
Date de publication:
2021
2021
Historique:
received:
11
08
2021
accepted:
23
09
2021
entrez:
27
10
2021
pubmed:
28
10
2021
medline:
28
10
2021
Statut:
epublish
Résumé
Secretome genes, encoding proteins secreted from the cell, are involved in the tumor immune response and correlated with levels of tumor mutation burden (TMB) in multiple tumors. This study aimed to identify core secretome genes and their potential association with immunomodulators and immune infiltration in high TMB groups across 14 major solid tumors through bioinformatics analysis. Multi-omics data for 14 major solid tumors were downloaded from The Cancer Genome Atlas (TCGA) database. Patients were divided into high TMB (TMB-high) and low TMB (TMB-low) groups using the median TMB values for each of the solid tumors. The CIBERSORT algorithm was conducted to estimate the proportion of 22 tumor-infiltrating immune cells (TIICs). Kaplan-Meier analysis and the log-rank test were utilized to screened prognosis-related genes. The correlations between core secretome genes and TIICs were analyzed using Spearman correlation coefficients. In TMB-high groups, multi-omics data analysis revealed that secretome genes were strongly associated with clinical characteristics, and 65 prognosis-related secretome genes were screened. Among the prognosis-related genes, 21 core secretome genes were identified, and strongly associated with five types of TIICs, namely activated NK cells, follicular helper T cells, CD8 T cells, and macrophages M0 and M2. Notably, three secretome genes ( We examined the prognostic significance of secretome genes and their potential association with immunomodulators and immune infiltration across 14 major solid tumors. In summary, three secretome genes (
Sections du résumé
BACKGROUND
BACKGROUND
Secretome genes, encoding proteins secreted from the cell, are involved in the tumor immune response and correlated with levels of tumor mutation burden (TMB) in multiple tumors. This study aimed to identify core secretome genes and their potential association with immunomodulators and immune infiltration in high TMB groups across 14 major solid tumors through bioinformatics analysis.
METHODS
METHODS
Multi-omics data for 14 major solid tumors were downloaded from The Cancer Genome Atlas (TCGA) database. Patients were divided into high TMB (TMB-high) and low TMB (TMB-low) groups using the median TMB values for each of the solid tumors. The CIBERSORT algorithm was conducted to estimate the proportion of 22 tumor-infiltrating immune cells (TIICs). Kaplan-Meier analysis and the log-rank test were utilized to screened prognosis-related genes. The correlations between core secretome genes and TIICs were analyzed using Spearman correlation coefficients.
RESULTS
RESULTS
In TMB-high groups, multi-omics data analysis revealed that secretome genes were strongly associated with clinical characteristics, and 65 prognosis-related secretome genes were screened. Among the prognosis-related genes, 21 core secretome genes were identified, and strongly associated with five types of TIICs, namely activated NK cells, follicular helper T cells, CD8 T cells, and macrophages M0 and M2. Notably, three secretome genes (
CONCLUSION
CONCLUSIONS
We examined the prognostic significance of secretome genes and their potential association with immunomodulators and immune infiltration across 14 major solid tumors. In summary, three secretome genes (
Identifiants
pubmed: 34703282
doi: 10.2147/IJGM.S333141
pii: 333141
pmc: PMC8527654
doi:
Types de publication
Journal Article
Langues
eng
Pagination
6755-6767Informations de copyright
© 2021 Wu et al.
Déclaration de conflit d'intérêts
The authors report no conflicts of interest in this work.
Références
Cancer Treat Rev. 2020 Sep;89:102084
pubmed: 32738738
BMC Syst Biol. 2014;8 Suppl 4:S11
pubmed: 25521941
Biomed Pharmacother. 2019 Oct;118:109228
pubmed: 31351430
Proc Natl Acad Sci U S A. 2020 May 5;117(18):9932-9941
pubmed: 32312819
Cell Rep. 2019 Feb 12;26(7):1965-1977.e4
pubmed: 30759403
Genome Res. 2003 Nov;13(11):2498-504
pubmed: 14597658
Cell Mol Immunol. 2020 Aug;17(8):807-821
pubmed: 32612154
N Engl J Med. 2011 Jun 30;364(26):2517-26
pubmed: 21639810
Patterns (N Y). 2021 Jun 30;2(8):100293
pubmed: 34430923
Nat Rev Immunol. 2017 Sep;17(9):559-572
pubmed: 28555670
Cancer Cell Int. 2021 May 25;21(1):276
pubmed: 34034744
Aging (Albany NY). 2020 Feb 9;12(4):3486-3501
pubmed: 32039832
Front Oncol. 2021 Mar 18;11:583083
pubmed: 33816226
PLoS Comput Biol. 2019 Mar 5;15(3):e1006701
pubmed: 30835723
Elife. 2018 Sep 04;7:
pubmed: 30179157
Br J Cancer. 2019 Jan;120(1):6-15
pubmed: 30413827
J Transl Med. 2021 Aug 21;19(1):361
pubmed: 34419075
Genome Res. 2018 Nov;28(11):1747-1756
pubmed: 30341162
Dis Markers. 2020 Aug 31;2020:8825997
pubmed: 32934754
J Hematol Oncol. 2018 Mar 15;11(1):39
pubmed: 29544515
Front Oncol. 2018 Mar 12;8:49
pubmed: 29594035
Brief Bioinform. 2021 May 20;22(3):
pubmed: 32814346
Cell Rep. 2019 Mar 5;26(10):2622-2635.e5
pubmed: 30840886
Nat Methods. 2015 May;12(5):453-7
pubmed: 25822800
Adv Anat Pathol. 2017 Nov;24(6):311-335
pubmed: 28777143
Nucleic Acids Res. 2015 Apr 20;43(7):e47
pubmed: 25605792
Biochim Biophys Acta. 2013 Nov;1834(11):2233-41
pubmed: 23542208
Mol Med Rep. 2019 Oct;20(4):3103-3112
pubmed: 31432110
Ann Oncol. 2016 Aug;27(8):1482-92
pubmed: 27069014
Front Cell Dev Biol. 2020 Jun 03;8:402
pubmed: 32582698
J Immunother Cancer. 2020 Sep;8(2):
pubmed: 32900863
Front Oncol. 2020 Mar 12;10:333
pubmed: 32226776
Bioinformatics. 2019 Oct 15;35(20):4200-4202
pubmed: 30903160
Oncoimmunology. 2019 Jun 16;8(9):e1629258
pubmed: 31428527
Int Immunopharmacol. 2020 Oct;87:106798
pubmed: 32693357
J Exp Clin Cancer Res. 2020 May 18;39(1):89
pubmed: 32423420
Cancer Treat Rev. 2018 Nov;70:178-189
pubmed: 30227299
Nucleic Acids Res. 2020 Jan 8;48(D1):D863-D870
pubmed: 31701128
Mol Cancer. 2019 Nov 6;18(1):155
pubmed: 31690319
BMC Immunol. 2019 Jan 11;20(1):4
pubmed: 30634925
Semin Cancer Biol. 2018 Feb;48:91-103
pubmed: 28467889
Nucleic Acids Res. 2019 Jan 8;47(D1):D607-D613
pubmed: 30476243
Math Biosci Eng. 2021 Jun 30;18(5):5921-5942
pubmed: 34517516
Ann Oncol. 2019 Jan 1;30(1):44-56
pubmed: 30395155
Semin Cell Dev Biol. 2018 Jun;78:93-101
pubmed: 28684183
J Invest Dermatol. 2019 Nov;139(11):2352-2358.e3
pubmed: 31176707
Nat Commun. 2019 Nov 29;10(1):5440
pubmed: 31784511
J Oncol. 2020 Aug 20;2020:9431560
pubmed: 32884571
PeerJ. 2020 Sep 30;8:e9736
pubmed: 33062410
Immunity. 2013 Oct 17;39(4):782-95
pubmed: 24138885
Nat Rev Immunol. 2020 Nov;20(11):651-668
pubmed: 32433532
Front Immunol. 2020 Jun 30;11:1218
pubmed: 32714316
PLoS One. 2019 Aug 5;14(8):e0219566
pubmed: 31381571
Mol Cancer Ther. 2017 Nov;16(11):2598-2608
pubmed: 28835386
Br J Cancer. 2017 Aug 8;117(4):451-460
pubmed: 28704840
J Clin Med. 2019 Dec 08;8(12):
pubmed: 31817953
EMBO Rep. 2014 Dec;15(12):1243-53
pubmed: 25381661
Cell Mol Immunol. 2021 Apr;18(4):842-859
pubmed: 33139907
CA Cancer J Clin. 2020 Mar;70(2):86-104
pubmed: 31944278
Trends Cancer. 2020 Jun;6(6):489-505
pubmed: 32460003