HTRA3 Is a Prognostic Biomarker and Associated With Immune Infiltrates in Gastric Cancer

Abstract

HtrA serine peptidase 3 (HTRA3) participates in multiple signal pathways and plays an important regulatory role in various malignancies; however, its role on prognosis and immune infiltrates in gastric cancer (GC) remains unclear. The study investigated HTRA3 expression in tumor tissues and its association with immune infiltrates, and determined its prognostic roles in GC patients. Patients with GC were collected from the cancer genome atlas (TCGA). We compared the expression of HTRA3 in GC and normal gastric mucosa tissues with Wilcoxon rank sum test. And logistic regression was used to evaluate the relationship between HTRA3 and clinicopathological characters. Gene ontology (GO) term analysis, Gene set enrichment analysis (GSEA), and single-sample Gene Set Enrichment Analysis (ssGSEA) was conducted to explain the enrichmental pathways and functions and quantify the extent of immune cells infiltration for HTRA3. Kaplan-Meier analysis and Cox regression were performed to evaluate the correlation between HTRA3 and survival rates. A nomogram, based on Cox multivariate analysis, was used to predict the impact of HTRA3 on prognosis. High HTRA3 expression was significantly correlated with tumor histological type, histological grade, clinical stage, T stage, and TP53 status (P < 0.05). HTRA3-high GC patients had a lower 10-year progression-free interval [PFI; hazard ratio (HR): 1.46; 95% confidence interval (CI): 1.02-2.08; P = 0.038], disease-specific survival (DSS; HR: 1.65; CI: 1.08-2.52; P = 0.021) and overall survival (OS; HR: 1.59; CI: 1.14-2.22; P = 0.006). Multivariate survival analysis showed that HTRA3 was an independent prognostic marker for PFI (HR: 1.456; CI: 1.021-2.078; P = 0.038), DSS (HR: 1.650; CI: 1.079-2.522; P = 0.021) and OS [hazard ratio (HR): 1.590; 95% confidence interval (CI):1.140-2.219; P = 0.006]. The C-indexes and calibration plots of the nomogram based on multivariate analysis indicated an effective predictive performance for GC patients. GSEA showed that High HTRA3 expression may activate NF-κB pathway, YAP1/WWTR1/TAZ pathway, and TGFβ pathway. There was a negative correlation between the HTRA3 expression and the abundances of adaptive immunocytes (T helper cell 17 cells) and a positive correlation with abundances of innate immunocytes (natural killer cells, macrophages etc.). HTRA3 plays a vital role in GC progression and prognosis and could be a moderate biomarker for prediction for survival after gastrectomy.

Keywords: HTRA3; bioinformatics; biomarker; gastric cancer; immune infiltration; prognosis.

Figure 1

Differential expression levels of HTRA3 in different malignancies and HTRA3-related differentially expressed genes (DEGs). (A) Increased or decreased HTRA3 of different cancers compared with normal tissues in the TCGA and GTEx database. (B, C) Differential expression levels of HTRA3 in GC. (D) A ROC curve to test the value of HTRA3 to identify GC tissues was created. (E, F) Volcano plots of the DEGs and heat map showing the top 10 DEGs.

Figure 2

Significantly enriched GO annotations of HTRA3 related genes in GC. (A) Top 7 of biological process enrichment related to HTRA3 related genes with bar graph. (B–G) Enrichment plots from the gene set enrichment analysis (GSEA). Several pathways and biological processes were differentially enriched in HTRA3-related GC, including activated NF-κB signals survival, YAP1 and WWTR1-TAZ stimulated gene expression, TGFβ pathway, RAS activation upon Ca²⁺ influx through NMDA receptor, active CIT activated by RHO GTPases, and RAC1 signaling pathway. NES, normalized enrichment score; p.adj, adjusted P value; FDR, false discovery rate.

Figure 3

The expression level of HTRA3 was associated with the immune infiltration in the tumor microenvironment. (A) Correlation between the relative abundances of 24 immune cells and HTRA3 expression level. The size of dots shows the absolute value of Spearman R. (B–G) Scatter plots and correlation diagrams showing the difference of NK cells, Macrophages, and Th17 cells infiltration level between HTRA3-high and -low groups.

Figure 4

Association with HTRA3 expression and clinicopathological characteristics, including (A) histological type, (B) histological grade, (C) pathologic stage, (D) T stage (E) TP53 status, (F) N stage, (G) M stage, (H) residual tumor, and (I) primary therapy outcome in GC patients in TCGA cohort. TCGA, The Cancer Genome Atlas; GC, gastric cancer; MT, mucinous type; DT, diffuse type; PT, papillary type; SRT, signet ring type; TT, tubular type.

Figure 5

Kaplan-Meier survival curves comparing the high and low expression of HTRA3 in GC. (A–C) Survival curves of OS, DSS, and PFI between HTRA3-high and -low patients with GC. (D–H) OS survival curves of stage III & IV, stage III, T4, N2&3, and M0 subgroups between HTRA3-high and -low patients with GC. (I) DSS survival curves of M0 subgroup between HTRA3-high and -low patients with GC. GC, gastric cancer; OS, overall survival; DSS, disease specific survival; PFI, progression free interval.

Figure 6

A quantitative method to predict GC patients’ probability of 1-, 3-, and 5-year OS. (A) A nomogram for predicting the probability of 1-, 3-, and 5- year OS for GC patients. (B) Calibration plots of the nomogram for predicting the probability of OS at 1, 3, and 5 years. GC, gastric cancer; OS, overall survival.