GC is a rapidly emerging and highly malignant form of cancer in terms of human cancer incidence, but the current conventional clinical therapies for GC remain significantly limited[19]. Therefore, it is imperative to explore novel and effective molecular markers to enhance the clinical outcomes of GC treatment. Immunotherapy has emerged as a pivotal approach in improving therapeutic efficacy against lung cancer, with mounting evidence suggesting an association between alterations in lysosomal biological functions and immune cells as well as cancer development[20–21]. Nevertheless, there exists a dearth of systematic investigation into differential LRGs among patients with STAD at present. Hence, this study aims to investigate the prognostic significance of LRGs in GC patients.
We first screened 7 key LRGs-TRIM29, EGF, GPC3, RETN, RNASE3, GRP, and PSAPL1, for the construction of prognostic risk model by univariate Cox, LASSO regression, and multivariate stepwise Cox regression. There is accumulating evidence supporting the relevance of these identified LRGs to malignant tumor prognosis. Studies have demonstrated that the TRIM29 gene acts as a crucial negative regulator of DNA viral and cytoplasmic DNA immune responses by targeting STING degradation [22], and there present an association between over-expression of the TRIM29 gene and squamous cell carcinomas of the skin as well as ovarian cancer[23–24].
EGF, the earliest discovered growth factor, plays a pivotal role in cell growth, differentiation, and proliferation. Previous study has reported a positive correlation between EGF presence in GC and infiltration as well as lymph node metastasis. Moreover, the detection of EGF in human GC may indicate an elevated level of cancer malignancy [25].
Although the precise function of the GPC3 gene remains elusive, an increasing body of evidence suggests that GPC3 serves as a promising target molecule for early diagnosis of hepatocellular carcinoma [26–28]. Moreover, a novel imaging strategy utilizing GPC3-targeted immune positron emission tomography has been developed to facilitate early diagnosis of hepatocellular carcinoma [29]. Consequently, further investigation into the role of GPC3 in GC is warranted, with the expectation that it may unveil a new target molecule for improving early detection of this malignancy.
The RETN gene is capable of encoding resistin and adipokines in the human body, exhibiting risk associations between resistin and RETN with susceptibility to breast cancer and type 2 diabetes mellitus [30–31]. Furthermore, it has been suggested that polymorphisms in RETN may contribute to an increased susceptibility to colon cancer disease [32].
RNASE3, a member of the RNASEA superfamily involved in host immunity, is expressed by leukocytes and possesses direct antimicrobial and immunomodulatory properties [33], prognostic models for idiopathic pulmonary fibrosis have been developed by researchers utilizing five types of immune cells, including RNASE3, resulting in improved outcomes [34].
GRP belongs to the belladonna peptide family of gastrin-releasing peptides, and it acts as an autocrine growth factor that stimulates the proliferation of various cancer cells and regulates numerous functions within the gastrointestinal and central nervous systems[35]. Furthermore, immune responses triggered by novel chimeric proteins targeting GRP have demonstrated inhibition of mouse mammary tumor cells EMT-6 [36]. Additionally, PSAPL1 has been identified as a valuable biodiagnostic marker for GC[37].
The risk score model was evaluated using training and validation sets. Firstly, the samples were divided into two groups based on the median risk score threshold, and the low-risk group exhibited a significantly longer survival time compared to the high-risk group. Additionally, the validity of the risk score's predictive performance was confirmed by K-M curve and ROC curve analysis. Univariate and multivariate Cox analyses further demonstrated that risk score served as an independent prognostic factors for GC, and a nomogram model incorporating risk score, stage, and age confirmed high accuracy in predicting 1-, 3-, and 5-year survival time. Finally, we verified the rationality of our risk model from biological functions. Immunoassay were conducted to explore functional differences between the high-risk and low-risk groups. The GO enrichment analyses of the BP term revealed differential results in the first four biological processes between the high-risk and low-risk groups, while only one of the first four pathways showed a significant difference in the KEGG pathway analyses between these two risk groups.
With advancements in science and medicine, immunotherapy has emerged as a groundbreaking approach to cancer treatment, with immune checkpoint inhibitors (ICIs) playing a pivotal role in altering the treatment and prognosis of gastric cancer [38]. Hence, we also investigated the association between the risk score model and immune checkpoints and revealed significant differential expression of immune checkpoint genes such as TNFSF4, CD276, NRP1, TNFRSF4, LAIR1, TNFRSF9, LAIR1, and CD28 between the high-risk and low-high groups. Infiltration of immune cells into tumor tissues along with their modulation of cytokine signaling significantly influences the biological function of cancer cells [39]. Our findings demonstrate a significantly higher expression level of CD4 T cells, B cells, memory B cells, and T cells in low-risk groups compared to high-risk groups. these results are consistent with previous research [40].
We observed that TTN and TP53 exhibited the highest mutation frequencies in both high-risk and low-risk groups, which are closely associated with immunotherapy[41]. TTN/TP53 co-mutations may be a potent predictor of OS and chemotherapeutic response in patients with lung cancer [42]. High mutation of TTN is positively correlated with the survival rate of GC patients, and the TTN gene is important in improving the level of immunity[43–44]. TP53 oncogene mutations are common in 50% of human cancers, and TP53 act as a transcription factor capable of directly regulating the expression of approximately 500 genes [45].
However, our study still has certain limitations. Firstly, we employ the traditional statistical model to identify the LRGs. Although our model demonstrates excellent performance in prognostic prediction for GC patients, leveraging machine learning or deep learning algorithms may yield more accurate predictive outcomes [46–47]. Secondly, further experimental exploration is required to elucidate the role of these 7 LRGs we identified in GC pathologic function. Addressing these aforementioned shortcomings will be the primary focus of our future work.