The role of ANGPTL4 in cancer: A meta-analysis of observational studies and multi-omics investigation

Abstract

Background: Angiopoietin-like protein 4 (ANGPTL4) plays a crucial role in processes such as angiogenesis, inflammation, and metabolism. Despite numerous studies suggesting its involvement in cancer, a definitive role remains unclear. We introduce the first comprehensive meta-analysis and pan-cancer bioinformatics study on ANGPTL4, aiming to unravel its implications across various cancer types.

Methods: Moderate-to high-quality observational studies were retrieved from PubMed, Scopus, and Embase. A meta-analysis was conducted using the R package "meta." Survival analysis was performed using GEPIA2 and TIMER2.0. Immune infiltration, mutational burden, and drug resistance analyses was done via GSCAlite. Co-expression and gene set enrichment analyses (GSEA) were carried out using cBioportal and enrichr, respectively.

Results: Increased ANGPTL4 expression was linked to worse tumor grade (OR = 1.51, P = 0.023), stage (OR = 2.42, P < 0.001), lymph node metastasis (OR = 1.76, P = 0.012), vascular invasion (OR = 2.16, P = 0.01), and lymphatic invasion (OR = 2.20, P < 0.001). Furthermore, ANGPTL4 expression was linked to worse OS (HR = 1.40, 95% CI: 1.29,1.50, P = 0.0001). Single gene level analysis revealed that ANGPTL4 upregulated epithelial-to-mesenchymal transition (EMT) in 23 different cancers. Immune infiltration varied between cancer types, but increased infiltration of cancer-associated fibroblasts was observed in most cancers. Mutation analysis revealed increased alterations in TP53 and CDKN2A in cohorts with ANGPTL4 alterations. GSEA of co-expressed genes revealed involvement in hypoxia, EMT, VEGF-A complex, TGF-B pathways, and extracellular matrix organization.

Conclusions: ANGPTL4 plays a significant role in tumor progression via its positive regulation of EMT and angiogenesis, while possibly harboring a TGF-B dependent role in systemic metastasis. Therefore, ANGPTL4 is a suitable target for future drug development.

Fig 1. PRISMA flowchart.

Fig 2. Forest plots for the clinicopathological factors. (A) Age (B) Gender (C) TNM staging (D) Histological differentiation (E) T staging (F) Tumor size (G) Lymph node metastasis (H) Lymphatic Invasion (I) Distant metastasis (J) Local recurrence (K) Vascular invasion.

Fig 3. Forest plots for the meta-analysis of ANGPTL4 and survival.

Fig 3 shows the forest plots for prognostic outcomes. (A) Forest plot for overall survival (B) Sub-group forest plot for overall survival (C) Forest plot for disease-free survival.

Fig 4. Multivariate cox regression and KM curves for survival based on ANGPTL4 expression. (A) Forest plot showing the multivariate HR for different cancers. (B) Table showing the multivariate HR values. (C) OS for CESC (D) OS for LGG (E) OS for LUAD (F) OS for Mesothelioma (G) OS for ACC (H) OS for STAD (I) OS for OV.

Fig 5. The landscape of ANGPTL4 alterations in cancer.

(A) Shows the most frequent alterations of ANGPTL4 in different cancers including mutations, variants, amplifications, deep deletions, high or low expression, or the multiplicity of alterations. (B) Shows the percentage of deleterious ANGPTL4 mutations in each TCGA cancer. (C) Shows the alteration frequency of oncogenic genes between patients who have ANGPTL4 alteration and those who do not. (D) Shows the percentage of ANGPTL4 CNV’s per cancer. (E) Shows the expression of ANGPTL4 in normal and tumor tissues in different cancers. *  in Fig 5C represents a q value <  0.05.

Fig 6. The correlation between immune infiltration and ANGPLT4 expression in cancer.

(A) Shows the immune infiltration of 24 different immune cells based on ANGPTL4 expression, 17 of the 24 are different subsets of T cells. (B) Shows the infiltration of Cancer-associated fibroblasts based on ANGPTL4 expression. *  =  p <  0.05, # =  FDR <  0.05.

Fig 7. Correlation between ANGPTL4 and cancer cell functional state at the single cell level.

(A) Dot plot graph showing the correlation between 11 cancer types and 14 different pathways based on ANGPTL4 expression. (B) Shows the single cell functional enrichment of RCC. (C) Shows the single cell functional enrichment for RB. (D) Shows the single cell functional enrichment of NSCLC. (E) Shows the single cell functional enrichment for melanoma. (F) Shows the single cell functional enrichment for BRCA. (G) Shows the single cell functional enrichment for GBM. (H) Shows the single cell functional enrichment for HNSCC. Legend: *** p<=0.001; ** p<=0.01; *  p<=0.05.

Fig 8. Protein-Protein Interaction Network, gene set enrichment analysis, and co-expressed genes with ANGPTL4 in cancer.

(A) Shows a PPI network for ANGPTL4 highlighting ANGPTL4’s interaction with other proteins. (B) Shows the GSEA of negatively co-expressed genes. (C) Shows the GSEA of positively co-expressed genes. (D) Shows the correlation between ANGPTL4 and TGFB response in metastatic cells. (E) Shows the correlation between ANGPTL4 and TGFB response in primary tumor cells. (F) Shows the GSVA pathway enrichment for the genes negatively co-expressed with ANGPTL4. (G) Shows the GSVA pathway enrichment for the genes positively co-expressed with ANGPTL4. *  =  p <  0.05, # =  FDR <  0.05.