The Comprehensive Analysis of Specific Proteins as Novel Biomarkers Involved in the Diagnosis and Progression of Gastric Cancer

Serum Cystatin S (CST4): A Novel Prognostic Marker for Gastric Cancer

Background

Serum Cystatin S (CST4), a secretory protein that inhibits cellular matrix degradation, significantly influences the tumor microenvironment and tumor progression. However, the prognostic value of serum CST4 in gastric cancer (GC) remains unclear. This study aims to explore serum CST4's utility in GC prognostic assessment.

Methods

A cohort of 334 patients with GC who underwent radical gastrectomy was assessed. Preoperative serum CST4 levels were measured alongside traditional tumor markers, correlating with clinical data and patient outcomes. The cohort was divided into training and test sets at a ratio of 3:1 for Cox regression analyses, which identified CST4 as an independent risk factor for overall survival (OS) and disease-free survival (DFS). A prognostic model was developed, validated with calibration curves, and its predictive value was evaluated using receiver operating characteristic (ROC) curves. In addition, CST4 expression was correlated with immune cell infiltration using data from The Cancer Genome Atlas (TCGA). Patients were stratified by median CST4 levels, and Kaplan-Meier curves for OS and DFS were plotted.

Results

Cystatin S was confirmed as an independent risk factor for OS and DFS. Integrating CST4 with traditional markers and TNM pathological staging significantly enhanced the predictive value for prognosis. Cystatin S's impact on tumor progression is likely mediated through modulation of the immune microenvironment, including immune suppression and evasion.

Conclusion

Cystatin S is an effective biomarker for GC prognostic assessment, assisting in the evaluation of prognosis and the selection of treatment strategies for patients with GC.

Evaluation of a three-gene methylation model for correlating lymph node metastasis in postoperative early gastric cancer adjacent samples

Background

Lymph node metastasis (LNM) has a profound impact on the treatment and prognosis of early gastric cancer (EGC), yet the existing evaluation methods lack accuracy. Recent research has underscored the role of precancerous lesions in tumor progression and metastasis. The objective of this study was to utilize the previously developed EGC LNM prediction model to further validate and extend the analysis in paired adjacent tissue samples.

Methods

We evaluated the model in a monocentric study using Methylight, a methylation-specific PCR technique, on postoperative fresh-frozen EGC samples (n = 129) and paired adjacent tissue samples (n = 129).

Results

The three-gene methylation model demonstrated remarkable efficacy in both EGC and adjacent tissues. The model demonstrated excellent performance, with areas under the curve (AUC) of 0.85 and 0.82, specificities of 85.1% and 80.5%, sensitivities of 83.3% and 73.8%, and accuracies of 84.5% and 78.3%, respectively. It is noteworthy that the model demonstrated superior performance compared to computed tomography (CT) imaging in the adjacent tissue group, with an area under the curve (AUC) of 0.86 compared to 0.64 (p < 0.001). Furthermore, the model demonstrated superior diagnostic capability in these adjacent tissues (AUC = 0.82) compared to traditional clinicopathological features, including ulceration (AUC = 0.65), invasional depth (AUC = 0.66), and lymphovascular invasion (AUC = 0.69). Additionally, it surpassed traditional models based on these features (AUC = 0.77).

Conclusion

The three-gene methylation prediction model for EGC LNM is highly effective in both cancerous and adjacent tissue samples in a postoperative setting, providing reliable diagnostic information. This extends its clinical utility, particularly when tumor samples are scarce, making it a valuable tool for evaluating LNM status and assisting in treatment planning.

Personalized Assessment for Cancer Prevention, Detection, and Treatment

The intention of this Special Issue is to highlight research that aims to recognize cancer's complexity to better prevent or treat its occurrence [...].

Unravelling diagnostic clusters and immune landscapes of disulfidptosis patterns in gastric cancer through bioinformatic assay

Disulfidptosis is a novel type of cell death mediated by SLC7A11-induced disulfide stress. Gastric cancer (GC) is a common malignant gastrointestinal tumor. Existing evidence shows that SLC7A11 can regulate cell death and improve the progression of GC, suggesting disulfidptosis may exist in the pathological process of GC. However, the underlying functions of disulfidptosis regulators in GC remain unknown. The dataset of GSE54129 was screened to comprehensively investigate the disulfidptosis-related diagnostic clusters and immune landscapes in GC. Totally 15 significant disulfidptosis regulators were identified via difference analysis between GC samples and controls. Then random forest model was utilized to assess their importance score (mean decrease Gini). Then a nomogram model was constructed, which could offer benefit to patients based on our subsequent decision curve analysis. All the included GC patients were divided into 2 disulfidptosis subgroups (clusterA and clusterB) according to the significant disulfidptosis regulators in virtue of consensus clustering analysis. The disulfidptosis score of each sample was calculated through PCA algorithms to quantify the disulfidptosis subtypes. Patients from clusterB exhibited lower disulfidptosis scores than those of patients in clusterA. In addition, we found that the cases in clusterB were closely associated with the immunity of activated CD4 T cell, etc., while clusterA was linked to immature dendritic cell, mast cell, natural killer T cell, natural killer cell, etc., which has a higher disulfidptosis score. Therefore, disulfidptosis regulators play an important role in the pathological process of GC, providing a promising marker and an immunotherapeutic strategy for future GC therapy.