NF-κB-Related Metabolic Gene Signature Predicts the Prognosis and Immunotherapy Response in Gastric Cancer

Abstract

Background. Sufficient evidence indicated the crucial role of NF-κB family played in gastric cancer (GC). The novel discovery that NF-κB could regulate cancer metabolism and immune evasion greatly increased its attraction in cancer research. However, the correlation among NF-κB, metabolism, and cancer immunity in GC still requires further improvement. Methods. TCGA, hTFtarget, and MSigDB databases were employed to identify NF-κB-related metabolic genes (NFMGs). Based on NFMGs, we used consensus clustering to divide GC patients into two subtypes. GSVA was employed to analyze the enriched pathway. ESTIMATE, CIBERSORT, ssGSEA, and MCPcounter algorithms were applied to evaluate immune infiltration in GC. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict patients’ response to immunotherapy. We also established a NFMG-related risk score by using the LASSO regression model and assessed its efficacy in TCGA and GSE62254 datasets. Results. We used 27 NFMGs to conduct an unsupervised clustering on GC samples and classified them into two clusters. Cluster 1 was characterized by high active metabolism, tumor mutant burden, and microsatellite instability, while cluster 2 was featured with high immune infiltration. Compared to cluster 2, cluster 1 had a better prognosis and higher response to immunotherapy. In addition, we constructed a 12-NFMG (ADCY3, AHCY, CHDH, GUCY1A2, ITPA, MTHFD2, NRP1, POLA1, POLR1A, POLR3A, POLR3K, and SRM) risk score. Followed analysis indicated that this risk score acted as an effectively prognostic factor in GC. Conclusion. Our data suggested that GC subtypes classified by NFMGs may effectively guide prognosis and immunotherapy. Further study of these NFMGs will deepen our understanding of NF-κB-mediated cancer metabolism and immunity.

Targeting one‐carbon metabolism for cancer immunotherapy

“…Unsupervised clustering based on many genes, including MTHFD2, contributed to predicting the response of gastric cancer patients to immunotherapy. 62 MTHFD2 expression was significantly connected to many immune checkpoints, including CTLA4, TIGIT, PD-L1, CD276, HAVCR2, PDCD1 and LAG3, in urothelial carcinomas of the bladder. 41 MTHFD2 promoted the expression of PD-L1, which led to the immune evasion of pancreatic cancer.…”

“…Thus, abnormal ACHY was associated with immunotherapy. Unsupervised clustering based on NF‐κB‐related metabolic genes, including ACHY, could be applied to predict the prognosis and response to immunotherapy of gastric cancer patients 62 …”

“…Moreover, MTHFD2 is significantly related to the prognosis of patient and the expression of immune checkpoints. Unsupervised clustering based on many genes, including MTHFD2, contributed to predicting the response of gastric cancer patients to immunotherapy 62 . MTHFD2 expression was significantly connected to many immune checkpoints, including CTLA4, TIGIT, PD‐L1, CD276, HAVCR2, PDCD1 and LAG3, in urothelial carcinomas of the bladder 41 .…”

CHDH, a key mitochondrial enzyme, plays a diagnostic role in metabolic disorders diseases and tumor progression

“…In recent years, the role of CHDH in medicine has attracted attention. Different studies have found that CHDH is closely associated with the development of several diseases, including malignancies ( Chen et al, 2022 ; Wei et al, 2022 ; Zhang et al, 2022 ), psychiatric disorders ( Chang et al, 2017 ; Truong et al, 2022 ), male infertility ( Johnson et al, 2012 ; Lazaros et al, 2012 ), tooth agenesis ( Mostowska et al, 2011 ) and hyperhomocysteinemia ( Kumar et al, 2009 ). In addition, when choline is used as a therapeutic agent, CHDH may be responsible for part of the rapid turnover of free choline so that free choline does not remain elevated and inhibits choline from exerting its pharmacological effects ( Haubrich and Gerber, 1981 ).…”

“…They then grouped GC patients by constructing a risk score (RS), and finally found that patients with lower CHDH expression in the high-risk group had poorer OS. Thus, the good performance of CHDH in predicting the OS of GC patients was validated ( Chen et al, 2022 ).…”