In the study, our data support that ARSB is up-regulated in STAD. Of note, the overall expression of ARSB in all the 40 STAD cell lines included in CCLE database was down-regulated. However, the expression level of ARSB was varied greatly among the different STAD cell lines, some of which have downregulated ARSB expression but the others have upregulated expression of ARSB (data not shown). The heterogeneity of the expression in cultured cell lines as well as a common inconsistency between tumor cell lines and real tumor tissues might account for the discrepancy. In most cancers, ARSB expression was significantly related with CNA, methylation levels and survival time. Furthermore, a variety of genes involved in MHC, immunosuppressive gene, chemokine, chemokine receptor were related with ARSB expression level. In STAD, ARSB was positively related with ESTIMATE Score, Immune Score, Stromal Score, and negatively related with Tumor Purity, TMB value, and MSI value. Meanwhile, we demonstrated that ARSB were related with 11 kinds of tumor-infiltrating immune cells, immune checkpoint genes including LAG3, TIGIT, and PDCD1. TNN mutation widely occurred in both high- and low-ARSB expression group. Finally, we demonstrated that ARSB was found to be related to a variety of drug sensitivities, including lapatinib, acetalax, and osimertinib. Altogether, ARSB might be a potential therapeutic target and prognostic biomarker for STAD patients.
CNA is defined as structurally variant regions, which is responsible for the diversity of genome in human beings. The situation of CNA is related with the development of complex diseases and cancer etiology (Shlien and Malkin, 2009). In gastric cancer, Liang et al. confirmed the role of CNA in prognostic, diagnostic or therapeutic significances, and targeting CNA may provide novel opportunities for personalized therapy (Liang et al., 2016). In functional analysis, we observed that the genes related with ARSB expression levels were significantly enriched in ECM-receptor interaction, osteoclast differentiation, and signaling by MET, etc. Among them, c-Met signaling is implicated in many kinds of human malignancies, including gastric cancer (Birchmeier et al., 2003), and the inhibitor of c-Met signaling has been recommended as the novel anticancer drug (Eder et al., 2009). Although no evidence provided the direct interaction between ARSB and these pathways in STAD, all these pathways play roles in cancer development (Bao et al., 2019). Meanwhile, ARSB, as a prognostic biomarker, has been reported in a variety of cancers, such as colorectal cancer (Kovacs et al., 2019b), prostate cancer (Feferman et al., 2017), and cutaneous melanoma (Wan et al., 2020). Here, ARSB expression had significantly correlative levels with these pathways, which might be possible mechanism for poor prognosis of STAD patients.
The clinic pathological significance of tumor microenvironment has been widely illustrated in predicting therapeutic efficacy for cancers. Especially, in recent years, tumor immunotherapy has been widely researched and accepted as the novel treatment strategy in clinic. PDCD1 has been recognized as an immune checkpoint inhibitor in the treatment of a variety of cancers and has been used in clinical treatment (Kraehenbuehl et al., 2022). LAG3 (CD223) has been believed as the third IR to be targeted in the clinic and a large number of clinical studies have shown that LAG3 plays a role in cancer treatment (Andrews et al., 2017). Besides, TIGIT has also been deemed as a new checkpoint receptor target for cancer immunotherapy (Dougall et al., 2017). Blockade of the checkpoint receptor TIGIT prevents NK cell exhaustion and elicits potent anti-tumor immunity (Zhang et al., 2018). Meanwhile, our study has shown that the expression of ARSB had a significant connection with immune checkpoint genes, including LAG3, PDCD1, and TIGIT. These data provide important evidence for ARSB as pan-cancer predictive biomarkers for ICI treatment. Our data supported the positive relationships between ADPRH expression level and infiltration level of CD4 + T cell, neutrophil, and Tfh cells, suggesting that ARSB might be involved in tumor immune infiltration in STAD. For gastric cancers, evidence from the study by Gong et al. (Gong et al., 2020) demonstrated that low tumor purity was related with unfavorable prognosis, and the gene signature based on stromal-immune score was also accepted as a prognosis stratification tool (Wang et al., 2019). Our data showed ARSB expression level was positively related with ESTIMATE Score, Immune Score, Stromal Score, and negatively related with Tumor Purity, suggesting ARSB might be important potential prognosis molecular in STAD. However, the prognostic value of ARSB and the mechanisms involved in the correlation with immune cells infiltration should be studied further in clinical practice.
Identification of a standard and reliable biomarker requires a large and rich dataset via strict and robust analysis methods, and this biomarker should be verified in different independent sample databases. Although a robust and reliable analysis method has been proposed to improve the reliability of analysis results (Wu and Ma, 2015; Wu et al., 2018), GEO database lacks the information on CNV, TMB, SNP, immune ingredient and drug sensitivity, etc thus making it infeasible to fully achieve the robustness of the analysis results. Only one GEO dataset (GSE84437) included clinical survival information of 431 STAD patients. We expanded further to verify the gene expression and survival analysis of ARSB in two independent datasets (GSE54129 and GSE84437). Our analysis results showed that the expression of ARSB in STAD was significantly up-regulated (Supplementary Fig. 1). Survival analysis showed that ARSB had no significant effect on the evaluation of clinical prognosis of patients with STAD (P = 0.27) (Supplementary Fig. 2), which was different from our analysis results in TCGA database. It should be recognized that different database samples are collected from different research groups and there are differences in collection methods and follow-up standards. Therefore, the survival time of each patient will be different due to the patients’ physical and mental health and the degree of treatment, which are also the potential factors for the different results of the two analyses. The results of dataset-based bioinformatics analysis still need to be statistically verified by clinical specimens after equivalent treatment and close follow-up information on prognosis.
In summary, we elucidated the effects of ARSB in malignant tumors including STAD through a series of bioinformatics analysis of gene expression data and detailed clinical information of cancer cases from multiple public databases. We found that in most cancers, ARSB expression was significantly correlated with CNA, methylation levels and overall survival time. In STAD, ARSB was positively correlated with ESTIMATE Score, Immune Score, Stromal Score, and negatively related with Tumor Purity, TMB value and MSI value. Meanwhile, ARSB are related with tumor-infiltrating immune cells and immune checkpoint genes. TNN mutation widely occurred in both high- and low-ARSB expression group. ARSB was found to be related to a variety of drug sensitivities. Therefore, ARSB might be a potential therapeutic target and prognostic biomarker for cancers, especially STAD.