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. 2022 Mar;71(3):747-759.
doi: 10.1007/s00262-021-03020-4. Epub 2021 Aug 16.

An immune-related prognostic signature for thyroid carcinoma to predict survival and response to immune checkpoint inhibitors

Pu Wu #  1 , Wei Sun #  1 , Hao Zhang  2
Affiliations

An immune-related prognostic signature for thyroid carcinoma to predict survival and response to immune checkpoint inhibitors

Pu Wu et al. Cancer Immunol Immunother. 2022 Mar.

Abstract

Thyroid carcinoma (THCA) is the most common endocrine malignancy, and its incidence is increasing worldwide. Several studies have explored whether the tumor immune microenvironment and immune-related genes (IRGs) influence the prognosis of patients with THCA and can be used to predict the response to immune checkpoint inhibitors (ICIs). We developed an IRG prognostic/risk signature using a bioinformatics method, and its predictive capacity was validated in patients in the test set and the total set. Subsequently, we analyzed the correlation between this IRG prognostic signature and tumor-infiltrating immune cells, tumor mutation burden (TMB), and immune checkpoint protein expression in patients with THCA. With a multivariate analysis, the IRG prognostic signature, which comprised eight IRGs, was identified as an independent prognostic factor. High-risk patients had poor overall survival compared with low-risk patients. Plasma cells, monocytes, and dendritic cells infiltrated differently according to the IRG prognostic signature. The low-risk group had a higher TMB and immunophenoscore (IPS), which indicated a better response to ICIs. The qRT-PCR validated eight IRGs with differential expression in thyroid cancer and normal tissues. We conclude that the IRG prognostic signature may be a useful tool to predict survival and response to ICIs. However, further testing is required to assess the predictive capacity of this IRG prognostic signature.

Keywords: Immune checkpoint; Immune signature; Prognosis; THCA; TMB.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Identification DE-IRGs and functional enrichment analyses of DE-IRGs. A Volcano plot of DEGs in THCA. B Venn diagram for the intersections of DEGs and IRGs. C Gene ontology analysis. D KEGG pathway enrichment analysis
Fig. 2
Fig. 2
Construction of immune-related prognostic signature. C Forest plot presenting the univariate Cox model results. A, B LASSO Cox regression analysis and multivariate Cox regression analysis. D Forest plot illustrating the multivariate Cox regression analysis of eight prognostic immune-related genes
Fig. 3
Fig. 3
Identification of eight immune-related genes prognostic signature in training, test, and entire sets. A The distribution of risk scores, B survival status, C the expression of eight prognostic IRGs in high- and low-risk groups, D Kaplan–Meier curve analysis of overall survival of THCA in high- and low-risk groups, E time-dependent ROC analysis in the training set. F The distribution of risk scores, G survival status, H 8-IRGs expression patterns in high- and low-risk groups, I Kaplan–Meier curve analysis of overall survival, J time-dependent ROC analysis in the test set. K The distribution of risk scores, L survival status, M 8-IRGs expression patterns in high- and low-risk groups, N Kaplan–Meier curve analysis of overall survival, O time-dependent ROC analysis in the entire set
Fig. 4
Fig. 4
The relationships between immune-related prognostic signature and clinicopathological factors. Time-dependent ROC curves analysis according to risk score and clinical factors. The AUC of 1 (A), 3 (B) and 5 year (C) to predict overall survival for THCA patients. (D) Age, (E) gender, (F) clinical stage, (G) T stage, (H) N stage, and (I) M stage. (J) Heatmap showed the expression of the prognostic IRGs and clinicopathologic factors in high- and low-risk groups
Fig. 5
Fig. 5
The relationships between tumor-infiltrating immune cells and immune-related risk signature. A The association between immune cells infiltration and the signature. B The association between immune cells infiltration (Plasma cells) and overall survival for THCA patients. The immune status in high- and low-risk groups. The immune status C, 16 immune cells D, 13 immune-related functions E and the distribution of HLA-related genes F in high- and low-risk groups
Fig. 6
Fig. 6
The relationships between mutation profile and TMB in high- and low-risk groups. A Mutation profile of THCA patients in high- and low-risk groups. B The difference of TMB in high- and low-risk groups. C The association of TMB and OS and PFI of THCA patients. D The association between IPS and the immune-related prognostic signature. E The expression of PD-1, PD-L1, PD-L2, CTLA4, TIGIT, and TIM-3 in high- and low-risk groups
Fig. 7
Fig. 7
The expression of eight IRGs in thyroid cancer tissues. The qRT-PCR results showed the mRNA expression of HSPA6 A, PPBP B, S100A11 C, AZU1 D, SEMA6B E, VGF F, IL20RA G, and FYN H in normal tissues and thyroid cancer tissues
Fig. 8
Fig. 8
The correlation between the RT-qPCR data and clinical features. A–C The correlation between AZU1 and clinical stage, N stage and extrathyroidal extension. D–E The association between S100A11 and clinical stage, age. F–G The correlation between the PPBP and T, N stage. H The relationship between VGF and age. I–J The association between SEMA6B and age, N stage
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