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. 2024 Feb 24;150(2):103.
doi: 10.1007/s00432-023-05552-x.

A novel EIF3C-related CD8+ T-cell signature in predicting prognosis and immunotherapy response of nasopharyngeal carcinoma

Rui Li #  1 , Yikai Wang #  1 , Xin Wen #  1   2 , Binglin Cheng  1 , Ruxue Lv  1 , Ruzhen Chen  1 , Wen Hu  1 , Yinglei Wang  3 , Jingwen Liu  3 , Bingyi Lin  3 , Haixiang Zhang  3 , Enting Zhang  3 , XinRan Tang  4
Affiliations

A novel EIF3C-related CD8+ T-cell signature in predicting prognosis and immunotherapy response of nasopharyngeal carcinoma

Rui Li et al. J Cancer Res Clin Oncol. .

Abstract

Purpose: At present, dysfunctional CD8+ T-cells in the nasopharyngeal carcinoma (NPC) tumor immune microenvironment (TIME) have caused unsatisfactory immunotherapeutic effects, such as a low response rate of anti-PD-L1 therapy. Therefore, there is an urgent need to identify reliable markers capable of accurately predicting immunotherapy efficacy.

Methods: Utilizing various algorithms for immune-infiltration evaluation, we explored the role of EIF3C in the TIME. We next found the influence of EIF3C expression on NPC based on functional analyses and RNA sequencing. By performing correlation and univariate Cox analyses of CD8+ Tcell markers from scRNA-seq data, we identified four signatures, which were then used in conjunction with the lasso algorithm to determine corresponding coefficients in the resulting EIF3C-related CD8+ T-cell signature (ETS). We subsequently evaluated the prognostic value of ETS using univariate and multivariate Cox regression analyses, Kaplan-Meier curves, and the area under the receiver operating characteristic curve (AUROC).

Results: Our results demonstrate a significant relationship between low expression of EIF3C and high levels of CD8+ T-cell infiltration in the TIME, as well as a correlation between EIF3C expression and progression of NPC. Based on the expression levels of four EIF3C-related CD8+ T-cell marker genes, we constructed the ETS predictive model for NPC prognosis, which demonstrated success in validation. Notably, our model can also serve as an accurate indicator for detecting immunotherapy response.

Conclusion: Our findings suggest that EIF3C plays a significant role in NPC progression and immune modulation, particularly in CD8+ T-cell infiltration. Furthermore, the ETS model holds promise as both a prognostic predictor for NPC patients and a tool for adjusting individualized immunotherapy strategies.

Keywords: CD8+ T cells; EIF3C; Immunotherapy response; Nasopharyngeal carcinoma; Prognosis.

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Conflict of interest statement

The authors have no relevant financial or non-financial interests to disclose.

Figures

Fig. 1
Fig. 1
Function of the core subunit C of eukaryotic translation initiation factor 3 (EIF3C) in nasopharyngeal carcinoma (NPC) tumor microenvironment (TME). A, E Comparison of the immune-evaluation results in EIF3C-high and EIF3C-low groups by the ESTIMATE algorithm in GSE102348 and TCGA-HNSC cohorts (ns: not significant, *p < 0.05, **p < 0.01, ****p < 0.0001). B, F Comparison of the proportion of cells related to TME between EIF3C-high and EIF3C-low groups by the MCPcounter algorithm in GSE102349 and TCGA-HNSC cohorts (ns: not significant, *p < 0.05, ****p < 0.0001). C The spearman correlation analysis between immune cell infiltration levels (MCPcounter) and expression levels of EIF3C in GSE102349. D The univariate COX regression analysis of immune cell infiltration levels (MCPcounter) in GSE102349
Fig. 2
Fig. 2
Functional analyses for the role of EIF3C in cell proliferation, migration, and invasion. A Relative EIF3C expression in NPC cell lines. B The transfection efficiency of si-eIF3c was verified in HONE1 and SUNE1 cells (*p < 0.05). C, D CCK8 and colony formation assays showed that EIF3C facilitated cell proliferation in HONE1 and SUNE1 (*p < 0.05). E Representative and quantified results of Transwell migration and invasion assays in HONE1 and SUNE1 cells with transfection of si-eIF3c or si-SCR (*p < 0.05)
Fig. 3
Fig. 3
Transfection of si-eIF3c induces a transcriptional response in S18 cells. A PCA analysis of all six samples, grouped by si-SCR (NC, pink) and si-eIF3c (blue). B Heatmap of top 25 DEGs ranked by p value and clustered by treatment. C Volcano plot of DEGs, which were colored by upregulated (red) and downregulated (blue) DEGs. Genes with │log2FC│ > 1 and FDR < 0.05 were regarded as DEGs. D A bubble plot of top 30 enriched KEGG pathways in the order of q-value using 106 DEGs
Fig. 4
Fig. 4
Construction of the EIF3C-related CD8+ T-cell signature (ETS) in GSE102349. A UMAP plot showed cell types in the GSE150430 dataset. B Coefficients of candidate genes were selected regarding lambda by lasso regression. Each curve meant a predictor. C NPC patients in GSE102349 were separated into two groups according to the median of the risk score. D The distribution of patients’ survival time and risk score in GSE102349. E, F Kaplan–Meier curves of PFS between the high- and low-risk groups, and the 1-, 2-, and 3-year area under the receiver operating characteristic (AUROC) curves depicted the performance of ETS for prognostic prediction efficacy in GSE102349
Fig. 5
Fig. 5
Validation of the prognostic values of ETS risk score in the validation set. A NPC patients in TCGA-HNSC were divided into two groups according to the median of the risk score. B The distribution of patients’ survival time and risk score in TCGA-HNSC. C, D Kaplan–Meier curves of OS between the high- and low-risk groups, and the AUROC for predicting 1-, 3-, and 5-year OS showed the performance of ETS for prognostic prediction efficacy in TCGA-HNSC. E, F GSEA plot showed five immune-related pathways were enriched in both GSE102349 and TCGA-HNSC cohorts
Fig. 6
Fig. 6
Identification of ETS risk score as an independent prognostic factor. Univariate and multivariate Cox regression analysis of the ETS risk score in the training (A) and validation (B) datasets
Fig. 7
Fig. 7
Performance of ETS for patients with immunotherapy. A, B Kaplan–Meier curves of OS between the high- and low-risk groups, and the AUROC depicted the performance of ETS for predicting immunotherapy efficacy in the IMvigor210 cohort. C, D Responses of high- and low-risk patients from IMvigor210 and GSE91061 cohorts to anti-PD-L1 therapy [complete response (CR), progressive disease (PD), partial response (PR), and stable disease (SD); *p < 0.05]
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