MUC16 as a serum-based prognostic indicator of prometastatic gastric cancer

Abstract

Metastatic gastric cancer (GC) presents significant clinical challenges due to its poor prognosis and limited treatment options. To address this, we conducted a targeted protein biomarker discovery study to identify markers predictive of metastasis in advanced GC (AGC). Serum samples from 176 AGC patients (T stage 3 or higher) were analyzed using the Olink Proteomics Target panels. Patients were retrospectively categorized into nonmetastatic, metastatic, and recurrence groups, and differential protein expression was assessed. Machine learning and gene set enrichment analysis (GSEA) methods were applied to discover biomarkers and predict prognosis. Four proteins (MUC16, CAIX, 5'-NT, and CD8A) were significantly elevated in metastatic GC patients compared to the control group. Additionally, GSEA indicated that the response to interleukin-4 and hypoxia-related pathways were enriched in metastatic patients. Random forest classification and decision-tree modeling showed that MUC16 could be a predictive marker for metastasis in GC patients. Additionally, ELISA validation confirmed elevated MUC16 levels in metastatic patients. Notably, high MUC16 levels were independently associated with metastatic progression in T3 or higher GC. These findings suggest the potential of MUC16 as a clinically relevant biomarker for identifying GC patients at high risk of metastasis.

Keywords: Gastric cancer; MUC16; Metastasis; Olink; Serum-based prognostic marker.

Figure 1

Serum samples collected from 176 patients with gastric cancer were analyzed using an Olink multiplexed high-throughput platform. The study comprised 98 nonmetastatic patients, 56 metastatic patients, and 22 recurrent patients. Machine learning and gene set enrichment analysis methods were applied to discover biomarkers and predict prognosis.

Figure 2

NPX comparison with eight statistically significant proteins. The control group refers to nonmetastatic patients. The largest difference in NPX values between the Control and Meta groups is shown with the Mucin-16 (MUC16) protein assay. Statistically significant differences are denoted against groups. The threshold of the nonparametric analysis of variance method was an adjusted P value of < 0.05.

Figure 3

Significantly enriched Gene Ontology (GO) terms. Dot plots and enrichment plots based on GSEA results show GO biological processes on the vertical line, and the area of the circle indicates gene counts. The P value is represented with the depth of colors. The right and left panels denoted as activated and suppressed indicate that represented groups have more activated pathways with higher protein expression levels. (A), (B) Comparison between Control and Meta groups with the enriched pathway ‘Response to interleukin-4’. (C), (D) Comparison between Control and Recur groups with the enriched pathway ‘Negative regulation of T-cell apoptotic process’. (E), (F) Comparison between Meta and Recur groups with the enriched pathway ‘Negative regulation of leukocyte apoptotic process’. R packages were used to generate the plots.

Figure 4

Importance of random forest models. (A) Evaluation of RF using the area under the curve measure. (B) Out-of-bag score means the error rate when random forest models are established. (C) Bar plot of variable importance of the top 25 protein assays is shown.

Figure 5

Evaluation of the prognostic value of the MUC16 level in GC serum. (A) The expression level of MUC16 in serum samples from EGC (n = 46) and AGC (n = 246) patients was measured with ELISA. (B) MUC16 unit increased in the metastasis group (n = 69) and the recurrence group (n = 22) compared to the AGC group (n = 155) without metastasis and recurrence. P values less than 0.05 are given one asterisk, and P values less than 0.0001 are given four asterisks. ns means P > 0.05.