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

Pawluczuk, E.; Łukaszewicz-Zając, Marta; Mroczko, Barbara

doi:10.3390/ijms24108833

Cited by 3 publications

(1 citation statement)

References 88 publications

(116 reference statements)

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“…Gastric cancer (GC) is a malignant gastrointestinal tumor that is characterized by strong chemotherapeutic resistance to current therapies due to the highly heterogeneous and immature phenotype of cancer cells during tumourgenesis and development, thus adversely affecting health and life quality [ 16 ]. Cases of GC are expected to increase to about 1.8 million by 2040, while the number of deaths due to GC worldwide will reach 1.3 million each year [ 17 ]. Due to the insidious nature of symptoms, patients used to be diagnosed with advanced and unresectable GC [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

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

Zhang,

Chen,

Lin

et al. 2023

Aging

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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.

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Section: Discussionmentioning

confidence: 99%

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

Zhang,

Chen,

Lin

et al. 2023

Aging

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

Chen,

Long,

Liu

et al. 2024

Front. Oncol.

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BackgroundLymph 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.MethodsWe 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).ResultsThe 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).ConclusionThe 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.

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Personalized Assessment for Cancer Prevention, Detection, and Treatment

Paleari

2024

IJMS

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

Cited by 3 publications

References 88 publications

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

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

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

Personalized Assessment for Cancer Prevention, Detection, and Treatment

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