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Meta-Analysis
. 2023 Jan 13;23(1):45.
doi: 10.1186/s12885-023-10532-y.

A meta-validated immune infiltration-related gene model predicts prognosis and immunotherapy sensitivity in HNSCC

Yinghe Ding #  1 , Ling Chu #  2 , Qingtai Cao #  1 , Hanyu Lei  1 , Xinyu Li  1 , Quan Zhuang  3   4
Affiliations
Meta-Analysis

A meta-validated immune infiltration-related gene model predicts prognosis and immunotherapy sensitivity in HNSCC

Yinghe Ding et al. BMC Cancer. .

Abstract

Background: Tumor microenvironment (TME) is of great importance to regulate the initiation and advance of cancer. The immune infiltration patterns of TME have been considered to impact the prognosis and immunotherapy sensitivity in Head and Neck squamous cell carcinoma (HNSCC). Whereas, specific molecular targets and cell components involved in the HNSCC tumor microenvironment remain a twilight zone.

Methods: Immune scores of TCGA-HNSCC patients were calculated via ESTIMATE algorithm, followed by weighted gene co-expression network analysis (WGCNA) to filter immune infiltration-related gene modules. Univariate, the least absolute shrinkage and selection operator (LASSO), and multivariate cox regression were applied to construct the prognostic model. The predictive capacity was validated by meta-analysis including external dataset GSE65858, GSE41613 and GSE686. Model candidate genes were verified at mRNA and protein levels using public database and independent specimens of immunohistochemistry. Immunotherapy-treated cohort GSE159067, TIDE and CIBERSORT were used to evaluate the features of immunotherapy responsiveness and immune infiltration in HNSCC.

Results: Immune microenvironment was significantly associated with the prognosis of HNSCC patients. Total 277 immune infiltration-related genes were filtered by WGCNA and involved in various immune processes. Cox regression identified nine prognostic immune infiltration-related genes (MORF4L2, CTSL1, TBC1D2, C5orf15, LIPA, WIPF1, CXCL13, TMEM173, ISG20) to build a risk score. Most candidate genes were highly expressed in HNSCC tissues at mRNA and protein levels. Survival meta-analysis illustrated high prognostic accuracy of the model in the discovery cohort and validation cohort. Higher proportion of progression-free outcomes, lower TIDE scores and higher expression levels of immune checkpoint genes indicated enhanced immunotherapy responsiveness in low-risk patients. Decreased memory B cells, CD8+ T cells, follicular helper T cells, regulatory T cells, and increased activated dendritic cells and activated mast cells were identified as crucial immune cells in the TME of high-risk patients.

Conclusions: The immune infiltration-related gene model was well-qualified and provided novel biomarkers for the prognosis of HNSCC.

Keywords: Head and neck squamous cell carcinoma; Immune cell infiltration; Immunotherapy sensitivity; Prognostic model; Tumor microenvironment.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Basic flow chart of this study
Fig. 2
Fig. 2
The landscape of immune infiltration microenvironment delineated by GSEA, GSVA and ESTIMATE analysis in HNSCC. A, B The GSEA results of significant pathways related to immune response and tumor microenvironment. C The heatmap of GSVA scores of biological processes involving immune response and tumor microenvironment in HNSCC and adjacent tissues. D The boxplot and statistical comparisons of immune and ESTIMATE scores of different tumor stages or grades. E The survival curves based on immune and ESTIMATE scores
Fig. 3
Fig. 3
Identification of immune infiltration-related gene modules by WGCNA. A, B Screening of the most suitable soft threshold to build a scale-free network with ideal scale independence and mean connectivity. C Clustering dendrogram of co-expression gene modules. D The correlation between modules and traits. The correlation coefficient and p-value are presented in each cell. E Eigengene dendrogram of gene modules and immune score. F, G Correlation scatter plots of module membership and gene significance in immune infiltration-related gene modules. H The eigengene adjacency heatmap of gene modules and immune score colored by white. I Functional enrichment of immune infiltration-related gene modules by GO and KEGG analysis. The top 10 biological processes and pathways are displayed. J Cnetplot of enrichment pathways and annotated genes
Fig. 4
Fig. 4
Identification of nine immune infiltration-related prognostic genes to build a risk score model. A Forest plot of prognostic module genes with P < 0.01 by univariate Cox regression. B, C Ten-time cross-validation of the LASSO model and coefficient profile of filtered prognostic genes. D Construction of the prognostic model by multivariate Cox regression. The hazard ratios (HRs) and 95% confidence intervals (CIs) of each candidate gene are shown. E The multivariate cox regression of the association between clinical factors (including the risk score) and survival. F The optimal cut point selected by the maximum standard log-rank statistics in HNSCC cohort. G PCA based on 277 immune infiltration-related genes showing different immune phenotypes in two risk groups. H The model gene expression heatmap combined with the distribution of risk scores and the survival of patients in two risk groups. I Kaplan-Meier survival curves of two risk groups in the whole and stage-divided HNSCC cohorts. J ROC curves based on risk score in HNSCC cohort within 1–5 years. K Nomogram combining risk score with clinical information. L ROC curves evaluating the predictive efficacy of the nomogram for the overall survival within 1–5 years
Fig. 5
Fig. 5
Investigation of the expression difference and prognostic effect of 9 candidate genes in TCGA HNSCC cohort. A The mRNA expression levels of 9 candidate genes in tumor and normal samples. B Kaplan-Meier survival curves for 9 candidate genes
Fig. 6
Fig. 6
Meta-analysis of the nine-gene signature model. A The Kaplan-Meier survival curve and hazard ratio (HR) based on TCGA-HNSCC. B The Kaplan-Meier survival curve and HR based on GSE65858. C The Kaplan-Meier survival curve and HR based on GSE41613. D The Kaplan-Meier survival curve and HR based on GSE686. E Meta-analysis of survival data for the nine-gene signature model. TE: estimate of treatment effect; SE: standard error; HR: hazard ratio; CI: confidence interval
Fig. 7
Fig. 7
Immunohistochemistry analysis of the protein expression of ISG20 and CTSL1 in HNSCC and normal tissues. A-E The expression of ISG20 was detected by immunohistochemistry in 5 patients with HNSCC (Magnification × 200). F The expression of ISG20 was detected by immunohistochemistry in normal head and neck squamous cell tissue (Magnification × 200). G-K The expression of CTSL1 was detected by immunohistochemistry in 5 patients with HNSCC (Magnification × 200). L The expression of CTSL1 was detected by immunohistochemistry in normal head and neck squamous cell tissue (Magnification × 200). AOD: average optical density; IOD: integral optical density
Fig. 8
Fig. 8
Immunohistochemistry of several prognostic signatures based on the HPA. A Protein expression levels of TBC1D2 in HNSCC and normal tissue. B Protein expression levels of WIPF1 in HNSCC and normal tissue. C Protein expression levels of TMEM173 in HNSCC and normal tissue. D Protein expression levels of C5orf15 in HNSCC and normal tissue
Fig. 9
Fig. 9
The profiles of immunotherapy sensitivity, immune infiltration and somatic mutation in HNSCC patients. A The correlation between the risk score and known biological processes and signaling pathways in tumor microenvironment. B The correlation between risk scores and tumor immune dysfunction and exclusion (TIDE) scores, myeloid-derived suppressor cell (MDSC), T cell exclusion scores, and T cell dysfunction scores. C The proportion of patients with different immunotherapy responses in risk groups from dataset GSE159067 and the difference of risk scores between immunotherapy response groups. D The expression levels of immune checkpoint genes in two risk groups. E The calculated proportion of 22 immune cells in two risk groups. F Heatmap showing the correlation significance between immune cells and model candidate genes. G The correlation between the risk score and tumor mutation burden (TMB) score. H The mutation landscape of HNSCC patients in two risk groups. The barplot represents the composition of mutation type and the percentage represents the mutation frequency of each gene. PD: progressive disease; SD: stable disease; PR: partial response; CR: complete response. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001
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