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. 2022 Mar 2:15:2329-2345.
doi: 10.2147/IJGM.S352330. eCollection 2022.

Identification of DTL as Related Biomarker and Immune Infiltration Characteristics of Nasopharyngeal Carcinoma via Comprehensive Strategies

Hehe Wang  1 , Junge Zhang  2
Affiliations

Identification of DTL as Related Biomarker and Immune Infiltration Characteristics of Nasopharyngeal Carcinoma via Comprehensive Strategies

Hehe Wang et al. Int J Gen Med. .

Abstract

Purpose: Although considerable progress has been made in basic and clinical research on nasopharyngeal carcinoma (NPC), the biomarkers of the progression of NPC have not been fully studied and described. This study was designed to identify potential novel biomarkers for NPC using integrated analyses and explore the immune cell infiltration in this pathological process.

Methods: Five GEO data sets were downloaded from gene expression omnibus database (GEO) and analysed to identify differentially expressed genes (DEGs), followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The four algorithms were adopted for screening of novel and key biomarkers for NPC, including random forest (RF) machine learning algorithm, least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE), and weighted gene co-expression network analysis (WGCNA). Lastly, CIBERSORT was used to assess the infiltration of immune cells in NPC, and the correlation between diagnostic markers and infiltrating immune cells was analyzed.

Results: Herein, we identified 46 DEGs, and enrichment analysis results showed that DEGs and several kinds of signaling pathways might be closely associated with the occurrence and progression of NPC. DTL was recognized as NPC-related biomarker. DTL, also known as retinoic acid-regulated nuclear matrix-associated protein (RAMP), or DNA replication factor 2 (CDT2), is reported to be correlated with the cell proliferation, cell cycle arrest and cell invasion in hepatocellular carcinoma, breast cancer and gastric cancer. Immune infiltration analysis demonstrated that macrophages M0, macrophages M1 and T cells CD4 memory activated were linked to pathogenesis of NPC.

Conclusion: In summary, we adopted a comprehensive strategy to screen DTL as biomarkers related to NPC and explore the critical role of immune cell infiltration in NPC.

Keywords: DTL; NPC; biomarkers; machine learning algorithm; nasopharyngeal carcinoma.

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Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

None
Graphical abstract
Figure 1
Figure 1
DEGs in the integrated dataset of NPC. (A) The volcano plots of DEGs, the red and green dots represent up-regulated and down-regulated genes, respectively. (B) The heatmap of DEGs.
Figure 2
Figure 2
Continued.
Figure 2
Figure 2
Functional enrichment analysis of DEGs. (A) Results of GO functional enrichment analysis of the DEGs, including BP, MF and CC. (B) KEEG enrichment analysis revealed signaling pathways highly associated with NPC. (C) The top five signaling pathways in normal nasopharyngeal tissue based on GSEA are shown. (D) GSEA showed that the top five signaling pathways were most related to NPC.
Figure 3
Figure 3
Continued.
Figure 3
Figure 3
Screening characteristic related biomarkers via comprehensive strategy. (A) The LASSO logistic regression algorithm was performed to retain the most predictive features. (B) Screening biomarkers based on random forest (RF) machine learning algorithm. (C) Results of screening biomarkers based on RF. (D) Results of screening biomarkers based on sSVM-RFE algorithm.
Figure 4
Figure 4
(A) The cluster dendrogram of genes in independent data sets. Branches of the cluster dendrogram of the most connected genes gave rise to eight gene coexpression modules. (B) Relationships of consensus modules with samples. Different color represents a specific module, containing a cluster of highly correlated genes. (C) Soft-threshold power determination for WGCNA by analysis of the scale-free fit index and mean connectivity for various soft-threshold powers.
Figure 5
Figure 5
(A) The venn diagram showed the intersection of diagnostic markers obtained by four algorithms. (B) ROC curves of DTL in the training dataset.
Figure 6
Figure 6
Validation of the diagnosis-related gene signature. (A) The expression of DTL in GSE53819. (B) ROC curves of DTL in GSE53819.
Figure 7
Figure 7
Continued.
Figure 7
Figure 7
Immune cells infiltration analysis. (A) Pattern of infiltration of 22 kinds of immune cells in normal and tumor groups. (B) The violin plot showed the difference in 22 infiltrating immune cells between NPC and normal nasopharyngeal tissue. (C) The correlation heatmap was drawn to display the correlations of 22 types of infiltrated immune cells. The size of color square represents correlation intensity, red represents the positive correlation, and blue represents the negative correlation.
Figure 8
Figure 8
Correlation between DTL and infiltrating immune cells. The lower the p-value, the more green the color, and the higher the p-value, the yellow the color.
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References

    1. Chen YP, Chan AT, Le QT, et al. Nasopharyngeal carcinoma. Lancet. 2019;394(10192):64–80. doi:10.1016/S0140-6736(19)30956-0 - DOI - PubMed
    1. Peng L, Liu JQ, Xu C, et al. The prolonged interval between induction chemotherapy and radiotherapy is associated with poor prognosis in patients with nasopharyngeal carcinoma. Radiat Oncol. 2019;14(1):9. doi:10.1186/s13014-019-1213-4 - DOI - PMC - PubMed
    1. Mao YP, Xie FY, Liu LZ, et al. Re-evaluation of 6th edition of AJCC staging system for nasopharyngeal carcinoma and proposed improvement based on magnetic resonance imaging. Int J Radiat Oncol Biol Phys. 2009;73(5):1326–1334. doi:10.1016/j.ijrobp.2008.07.062 - DOI - PubMed
    1. Chan KC, Hung EC, Woo JK, et al. Early detection of nasopharyngeal carcinoma by plasma Epstein-Barr virus DNA analysis in a surveillance program. Cancer. 2013;119(10):1838–1844. doi:10.1002/cncr.28001 - DOI - PubMed
    1. King AD, Woo JK, Ai QY, et al. Complementary roles of MRI and endoscopic examination in the early detection of nasopharyngeal carcinoma. Ann Oncol. 2019;30(6):977–982. doi:10.1093/annonc/mdz106 - DOI - PubMed