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. 2022 Jan 21;12(2):140.
doi: 10.3390/jpm12020140.

High Expression of Interferon Pathway Genes CXCL10 and STAT2 Is Associated with Activated T-Cell Signature and Better Outcome of Oral Cancer Patients

Yun-Cian Huang  1 , Jau-Ling Huang  2 , Lu-Chia Tseng  2 , Ping-Hung Yu  3 , Si-Yun Chen  1 , Chang-Shen Lin  1   4   5   6
Affiliations

High Expression of Interferon Pathway Genes CXCL10 and STAT2 Is Associated with Activated T-Cell Signature and Better Outcome of Oral Cancer Patients

Yun-Cian Huang et al. J Pers Med. .

Abstract

To improve the survival rate of cancer patients, biomarkers for both early diagnosis and patient stratification for appropriate therapeutics play crucial roles in precision oncology. Investigation of altered gene expression and the relevant molecular pathways in cancer cells are helpful for discovering such biomarkers. In this study, we explore the potential prognostic biomarkers for oral cancer patients through systematically analyzing five oral cancer transcriptomic data sets (TCGA, GSE23558, GSE30784, GSE37991, and GSE138206). Gene Set Enrichment Analysis (GSEA) was individually applied to each data set and the upregulated Hallmark molecular pathways of each data set were intersected to generate 13 common pathways including interferon-α/γ pathways. Among the 5 oral cancer data sets, 43 interferon pathway genes were commonly upregulated and 17 genes exhibited prognostic values in TCGA cohort. After validating in another oral cancer cohort (GSE65858), high expressions of C-X-C motif chemokine ligand 10 (CXCL10) and Signal transducer and activator of transcription 2 (STAT2) were confirmed to be good prognostic biomarkers. GSEA of oral cancers stratified by CXCL10/STAT2 expression showed that activation of T-cell pathways and increased tumor infiltration scores of Type 1 T helper (Th1) and CD8+ T cells were associated with high CXCL10/STAT2 expression. These results suggest that high CXCL10/STAT2 expression can predict a favorable outcome in oral cancer patients.

Keywords: CXCL10; STAT2; T cells; biomarker; interferon; oral cancer; prognosis.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
The study pipeline. T, tumor. N, normal. GSEA, gene set enrichment analysis. GOBP, Gene Ontology Biological Process.
Figure 2
Figure 2
Activation of interferon pathways in the 5 oral cancer data sets. (A) Intersection of the upregulated molecular pathways among the 5 oral cancer data sets. (B,C) Enrichment plots of interferon-α (B) and interferon-γ (C) pathways in the 5 oral cancer data sets.
Figure 3
Figure 3
Identification of the 43 interferon pathway genes commonly upregulated in the 5 oral cancer data sets. (A) Intersection of the core enriched genes in interferon-α and interferon-γ pathways of each data set. (B) Heat map showing the differential expressions of the 43 common interferon pathway genes in tumor (T) and normal (N) tissues of TCGA oral cancer data set.
Figure 4
Figure 4
Kaplan-Meier plots of overall survival based on the expressions of interferon pathway genes in TCGA (A-1,A-2) and GSE65858 (B) oral cancer cohorts. The best cut-off values were computed using ROC curve analysis on the Kaplan-Meier plotter (http://kmplot.com/analysis/ accessed between 15 November 2020 and 4 April 2021) online tool. Only the plots with p < 0.05 in TCGA (17 out of the 43 genes) and GSE65858 (3 out of the 17 significant genes in TCGA) data sets are shown.
Figure 4
Figure 4
Kaplan-Meier plots of overall survival based on the expressions of interferon pathway genes in TCGA (A-1,A-2) and GSE65858 (B) oral cancer cohorts. The best cut-off values were computed using ROC curve analysis on the Kaplan-Meier plotter (http://kmplot.com/analysis/ accessed between 15 November 2020 and 4 April 2021) online tool. Only the plots with p < 0.05 in TCGA (17 out of the 43 genes) and GSE65858 (3 out of the 17 significant genes in TCGA) data sets are shown.
Figure 5
Figure 5
The connection between CXCL10 and STAT2 and the prognostic value based on their coordinated expression. (A) Ingenuity Pathway Analysis shows the connection between CXCL10 and STAT2. (B) The Spearman correlation between the expressions of CXCL10 and STAT2. (C,D) Kaplan-Meier plots of overall survival based on the coordinated expression of CXCL10 and STAT2 (HH vs. LL) in TCGA (C) and GSE65858 (D) oral cancer cohorts. The best cut-off values were computed using ROC curve analysis on the Kaplan-Meier plotter online tool. HH, both high. LL, both low.
Figure 6
Figure 6
Enrichment plots of GOBP T-cell pathways in TCGA (A) and GSE65858 (B) oral cancer data sets based on the coordinated expression of CXCL10 and STAT2 (HH vs. LL). The case numbers of HH vs. LL used for GSEA were 108 vs. 144 for TCGA and 34 vs. 22 for GSE65858. GOBP, Gene Ontology Biological Process.
Figure 7
Figure 7
Tumor infiltration of Th1 (A,B) and CD8+ T cells (C,D) in TCGA (A,C) and GSE65858 (B,D) oral cancer data sets based on the coordinated expression of CXCL10 and STAT2 (HH vs. LL). The analysis was based on xCell algorithm (https://xcell.ucsf.edu/ (accessed on 4 April 2021). HH, both high. LL, both low.
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