In clinical practice, the diagnosis and prognosis prediction of GC patients primarily rely on various methods [18]. Initially, doctors gather preliminary information by thoroughly understanding the patient’s symptoms and conducting a physical examination [19]. Subsequently, imaging examinations, such as gastroscopy and X-ray, provide detailed information about the tumor’s structure. Blood biomarker tests, including CA 19-9 and CEA, serve as complementary tools for diagnosis and predicting disease progression [20, 21]. Additionally, pathological examinations involve obtaining tissue specimens through biopsies to determine histological types and assess lymph node involvement. Molecular biomarker testing, including HER2 and EGFR, provides deeper molecular insights that aid in formulating personalized treatment plans [22, 23]. The TNM staging system is employed to stage cancer, evaluating the severity of the disease and predicting prognosis [24, 25].

Targeted treatment strategies, such as anti-HER2 therapy, focus on specific molecular markers for treatment. However, these methods also have limitations. Gastroscopy is invasive and may not be suitable for all patients. Imaging examinations have limited resolution and may miss early or small lesions. Blood biomarker specificity is often constrained, and levels may be elevated in other diseases. Pathological examinations require tissue sampling, which can limit access to deep or hard-to-reach areas. Molecular biomarker analysis involves complex techniques and higher costs. The TNM staging system does not account for tumor molecular characteristics, and targeted treatments are only effective for patients with specific target markers.

MRNA has diverse potential applications in tumor diagnosis and prognosis prediction. First, through comprehensive molecular analysis, distinct mRNA expression patterns associated with specific cancer types or subtypes can be identified in tumor tissues, providing potential biomarkers for early cancer diagnosis and subtype classification. Second, by analyzing mRNA expression levels, a more accurate assessment of cancer prognosis can be made, offering valuable insights for the development of personalized treatment plans by healthcare professionals [26]. Additionally, mRNA analysis aids in determining the molecular subtypes of tumors, providing essential information for understanding the biological heterogeneity of cancer [27, 28]. In this study, we analyzed transcriptome data from GC patients in the TCGA datasets and identified 598 upregulated genes and 506 downregulated genes. Furthermore, functional enrichment analysis revealed that the differentially expressed genes are associated with activated biological processes such as the p53 signaling pathway, immune responses, and viral infections. In contrast, biological processes related to cell signaling, neural system functions, and metabolism appear to be suppressed. These findings provide valuable insights for further exploring the relationship between gene expression changes and specific physiological conditions or disease states.

We then identified survival-related differentially expressed genes (DEMs) in GC patients by integrating TCGA sequencing data with clinical information. Ten DEMs, including CLRN3, BCL11B, PDSS1, PRSS3, MYB, ADAMTS12, EPCAM, MAP7, F5, and VCAN, were found to be significantly associated with survival outcomes in GC patients. Among these, ADAMTS12, F5, and VCAN stood out due to their consistent upregulation in GC specimens, which correlated with poor prognosis. Pan-cancer analysis further highlighted the dysregulated expression of ADAMTS12, VCAN, and F5 across multiple tumor types, emphasizing their potential roles in tumorigenesis. In this analysis, ADAMTS12, F5, and VCAN were associated with both overall and disease-free survival in various cancers, reinforcing their prognostic significance. The upregulation of these genes in GC and their consistent association with poor prognosis underscores their clinical relevance in GC and beyond. Additionally, we developed a prognostic model using F5 and VCAN, which demonstrated a strong ability to predict clinical outcomes in GC patients. Our findings not only provide potential prognostic biomarkers for GC but also highlight the broader implications of ADAMTS12, F5, and VCAN in cancer biology.

Among ADAMTS12, F5, and VCAN, our attention focused on ADAMTS12. ADAMTS12 is an enzyme protein belonging to the metalloproteinase family, playing a crucial role in the extracellular matrix [29]. The term “A Disintegrin-like” in its name signifies its association with the ADAM family (A Disintegrin and Metalloproteinase), while “Thrombospondin Motifs” refers to the presence of domains similar to those found in thrombospondin. The protein encoded by ADAMTS12 exhibits metalloproteinase activity, allowing it to cleave and degrade protein molecules within the extracellular matrix [30, 31]. In both physiological and pathological conditions, ADAMTS12 plays a crucial role in various biological processes, including cell migration, tissue repair, angiogenesis, and the metabolism of collagen and proteoglycans. Its regulated activity is closely linked to the onset and progression of several diseases, including cancer, arthritis, and cardiovascular disorders [32,33,34]. In the realm of cancer research, alterations in the expression levels of ADAMTS12 are often observed, correlating with aspects of tumor initiation, progression, and prognosis. Through mechanisms influencing extracellular matrix remodeling, cell–cell interactions, and signaling pathway regulation, ADAMTS12 likely plays a role in the biological processes of tumors [35,36,37]. In addition, Chen et al. reported that analyzed the expression of ADAMTS12 in GC tissues and its involvement in the glycolysis pathway using bioinformatics and experimental methods. The findings demonstrated that ADAMTS12 promotes the proliferation and glycolysis of GC cells, while metformin can inhibit these effects. In vivo experiments further confirmed that metformin suppresses the proliferation and glycolysis of GC cells via ADAMTS12, suggesting that ADAMTS12 could be a potential target for metformin therapy in GC [38]. Moreover, Hou et al. found that ADAMTS12 expression was significantly elevated in gastric cancer samples and correlated with poor prognosis. Gene Set Enrichment Analysis (GSEA) indicated that high ADAMTS12 expression was associated with cancer and immune-related pathways, while low expression was linked to the oxidative phosphorylation pathway. Additionally, CIBERSORT analysis revealed a positive correlation between ADAMTS12 expression and macrophage infiltration, along with a negative correlation with T follicular helper cells, suggesting its role in modulating the tumor microenvironment and metabolic reprogramming in gastric cancer [39]. In this study, we also reported that ADAMTS12 was highly expressed in GC specimens and predicted a poor prognosis. Importantly, we performed RT-PCR and western blot, confirming the expression of ADAMTS12 was distinctly increased in GC specimens, which was consistent with the above findings. Our findings suggested ADAMTS12 as a novel prognostic and diagnostic biomarker for GC patients. Moreover, we performed in vitro assays and found that knockdown of ADAMTS12 distinctly suppressed the proliferation and invasion of GC cells, suggesting it as a tumor promotor in GC.

There are several limitations that should be acknowledged in this study. Firstly, although our analysis extensively utilized bioinformatics tools, including functional enrichment, survival analysis, and the construction of a prognostic signature, the absence of in vivo validation is a notable limitation. In vivo experiments are essential for confirming the functional roles of genes like ADAMTS12, F5, and VCAN in GC progression. Secondly, while the clinical information provided by the TCGA datasets offers valuable insights, it is important to recognize the inherent heterogeneity among GC patients. Variations in clinical characteristics, treatment regimens, and response patterns among individuals may affect the robustness and generalizability of our findings in clinical settings. Thirdly, the prognostic signature developed using LASSO regression, while offering a predictive model, introduces a certain level of complexity. The limited number of genes selected (VCAN and F5) may not fully capture the multifaceted nature of GC prognosis. Furthermore, the predictive performance of the model should be validated in independent cohorts to confirm its reliability.