Pan-Cancer Analysis of IGF-1 and IGF-1R as Potential Prognostic Biomarkers and Immunotherapy Targets

Abstract

Aim: Insulin-like growth factor-1 receptor (IGF-1R) is one of the main members of the tyrosine protein kinase receptor family. This receptor binds insulin-like growth factor-1 (IGF-1) with a high affinity. IGF-1 is a member of a family of proteins involved in mediating growth and development. However, the correlations of IGF-1 and IGF-1R to prognosis and tumor-infiltrating lymphocytes in different cancers remain unclear.

Method: This research comprehensively analyzed the expression pattern of IGF-1 and IGF-1R and the influence of IGF-1 and IGF-1R on clinical significance in prognosis prediction among 33 types of malignancies using The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) databases. The correlation between IGF-1, IGF-1R, and cancer immunity was explored.

Results: IGF-1 and IGF-1R displayed inconsistent gene expression levels among diverse cancer cell lines. Typically, high expression level of IGF-1 and IGF-1R was detected in most malignant tumors. High expression of IGF-1 was closely bound up with the unfavorable overall survival (OS) for patients in BLCA, CHOL, and LAML upon Cox and Kaplan-Meier analyses. While high expression of IGF-1R was closely bound up with the unfavorable overall survival (OS) for patients in BLCA, LIHC, and LUAD. Furthermore, high expression level of IGF-1 and IGF-1R were closely connected with high degrees of tumor infiltrates, including CD4+ T cell, dendritic cells, and macrophages. In addition, we found that IGF-1 was commonly positively correlated with the expression of gene markers including LAIR1, ICOS, CD40LG, CTLA4, CD48, CD28, CD200R1, HAVCR2, and CD86. Whereas, IGF-1R was commonly positively correlated with the expression of gene markers including NRP1 and CD276. More importantly, IGF-1 and IGF-1R expression were correlated with tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methyltransferase (DNMT) of different types of cancers.

Conclusions: The impact of high IGF-1 and IGF-1R on prognosis and immune infiltrates differs across cancer types. Anti-IGF-1R therapy may inhibit tumor growth and contribute to immunotherapy in LIHC and KIRC.

Keywords: IGF-1; IGF-1R; immunity; pan-cancer; prognostic biomarker.

Figure 1

IGF-1/IGF-1R expression levels in different types of human cancers. The expression levels of IGF-1 between tumor and normal tissues were compared in 20 cancer types based on the TCGA database (A) and 27 cancer types based on the integrated database from TCGA and GTEx datasets (B). The expression levels of IGF-1R between tumor and normal tissues were compared in 20 cancer types based on the TCGA database (C) and 27 cancer types based on the integrated database from TCGA and GTEx datasets (D). Consistent high expression level of IGF-1 could be seen in normal tissues than most types of tumor based on both comparisons, and significant decreased expression of IGF-1 could be seen in tumor samples including ACC, BLCA, BRCA, CESC, CHOL, COAD, ESCA, LAML, LGG, LIHC, LUAD, OV, PRAD, READ, SKCM, STAD, TGCT, THCA, UCEC, and UCS based on the integrated database. Consistent high expression level of IGF-1R could be seen in most types of tumor than normal tissue based on the both comparisons, and significant increased expression of IGF-1R could be seen in tumor samples including BRCA, CHOL, COAD, ESCA, GBM, HNSC, LAML, LGG, LIHC, LUAD, LUSC, PAAD, PRAD, SKCM, STAD, TGCT, and THCA based on the integrated database. “*, **, ***” means p < 0.05, p < 0.01 and p < 0.001, respectively.

Figure 2

Selected Kaplan-Meier plots and forest plot comparing the high and low expressions of IGF-1 on overall survival across different cancers. (A–C) Kaplan-Meier method showed high expression of IGF-1 correlated with unfavorable prognosis in BLCA, CHOL, and LAML. (D) Kaplan-Meier method showed high expression of IGF-1 correlated with favorable prognosis in SARC. (E) Forest plot displaying the impact of high expression of IGF-1 on OS across 33 cancer types using Cox regression model. Confidence level is shown in dashed lines.

Figure 3

Selected Kaplan-Meier plots and forest plot comparing the high and low expressions of IGF-1R on overall survival across different cancers. (A–C) Kaplan-Meier method showed high expression of IGF-1R correlated with unfavorable prognosis in BLCA, LIHC, and LUAD. (D, E) Kaplan-Meier method showed high expression of IGF-1R correlated with favorable prognosis in LAML and KIRC. (F) Forest plot displaying the impact of high expression of IGF-1R on OS across 33 cancer types using Cox regression model.

Figure 4

Correlation of IGF-1 expression level with immune infiltration level in BLCA, BRCA, and CHOL. Correlation of IGF-1R expression level with immune infiltration level in BLCA, LIHC, and PRAD. (A–C) IGF-1 expression is significantly positively correlated with CD4+ T cell, dendritic cell, and macrophage infiltration in BLCA, BRCA, and CHOL. (D–F) IGF-1R expression is significantly positively correlated with CD4+ T cell, dendritic cell, and macrophage infiltration in BLCA, LIHC, and PRAD.

Figure 5

Correlation of IGF-1/IGF-1R expression with estimate score. (A–C) IGF-1 expression significantly correlated with stromal scores in esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), and lower-grade glioma (LGG). (D–F) IGF-1 expression significantly correlated with immune scores in glioblastoma multiforme (GBM), lower-grade glioma (LGG), and lung squamous cell carcinoma (LUSC). (G–I) IGF-1R expression significantly correlated with stromal scores in liver hepatocellular carcinoma (LIHC), lower-grade glioma (LGG), and lung squamous cell carcinoma (LUSC). (J–L) IGF-1R expression significantly correlated with immune scores in breast invasive carcinoma (BRCA), kidney renal clear cell carcinoma (KIRC), and lung adenocarcinoma (LUAD).

Figure 6

Correlation of IGF-1 (A) and IGF-1R (B) expressions with expression of immune checkpoint genes across 33 cancer types. “*, **, ***” means significant correlation p < 0.05, p < 0.01 and p < 0.001, respectively.

Figure 7

Radar map plotting the correlation of tumor mutation burden (TMB) (A) and microsatellite instability (MSI) (B) with IGF-1 expression across 33 cancer types. Radar map plotting the correlation of tumor mutation burden (TMB) (C) and microsatellite instability (MSI) (D) with IGF-1R expression across 33 cancer types.

Figure 8

Functional Enrichment of KEGG and HALLMARK terms on IGF-1 and IGF-1R through GSEA. The top 3 negatively and positively enriched KEGG terms on IGF-1 are displayed in (A, B), respectively. The top 3 negatively and positively enriched KEGG terms on IGF-1 are displayed in (C, D), respectively. The top 3 negatively and positively enriched KEGG terms on IGF-1R are displayed in (E, F), respectively. The top 3 negatively and positively enriched KEGG terms on IGF-1R are displayed in (G, H), respectively.