Prognostic Model and Immune Infiltration of Ferroptosis Subcluster-Related Modular Genes in Gastric Cancer

Abstract

Background. Gastric cancer (GC) is one of the gastrointestinal tumors with the highest mortality rate. The number of GC patients is still high. As a way of iron-dependent programmed cell death, ferroptosis activates lipid peroxidation and accumulates large reactive oxygen species. The role of ferroptosis in GC prognosis was underrepresented. The objective was to investigate the role of ferroptosis-related genes (FRGs) in the prognosis and development of GC. Methods. Datasets of GC patients were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database that include clinical information and RNA seq data. Through nonnegative matrix factorization (NMF) clustering, we identified and unsupervised cluster analysis of the expression matrix of FRGs. And we constructed the co-expression network between genes and clinical characteristics by consensus weighted gene co-expression network analysis (WGCNA). The prognostic model was constructed by univariate and multivariate regression analysis. The potential mechanisms of development and prognosis in GC were explored by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene ontology (GO), tumor immune microenvironment (TIME), and tumor mutation burden (TMB). Results. Two molecular subclusters with different expression patterns of FRGs were identified, which have significantly different survival states. Ferroptosis subcluster-related modular genes were identified by WGCNA. Based on 8 ferroptosis subcluster-related modular genes (collagen triple helix repeat containing 1 (CTHRC1), podoplanin (PDPN), procollagen-lysine,2-oxoglutarate 5-dioxygenase 2 (PLOD2), glutamine-fructose-6-phosphate transaminase 2 (GFPT2), ATP-binding cassette subfamily A member 1 (ABCA1), G protein-coupled receptor 176 (GPR176), serpin family E member 1 (SERPINE1), dual specificity phosphatase 1 (DUSP1)) and clinicopathological features, a nomogram was constructed and validated for their predictive efficiency on GC prognosis. Through receiver operating characteristic (ROC) analysis, the results showed that the area under the curve (AUC) of 1-, 3-, and 5-year survival were 0.721, 0.747, and 0.803, respectively, indicating that the risk-scoring model we constructed had good prognosis efficacy in GC. The degree of immune infiltration in high-risk group was largely higher than low-risk group. It indicated that the immune cells have a good response in high-risk group of GC. The TMB of high-risk group was higher, which could generate more mutations and was more conducive to the body’s resistance to the development of cancer. Conclusion. The risk-scoring model based on 8 ferroptosis subcluster-related modular genes has shown outstanding advantages in predicting patient prognosis. The interaction of ferroptosis in GC development may provide new insights into exploring molecular mechanisms and targeted therapies for GC patients.

What is new in cancer-associated fibroblast biomarkers?

“…In addition to being expressed in CAFs, GFPT2 is also expressed in tumor parenchymal cells. The high expression of GFPT2 is closely related to invasion, metastasis, drug sensitivity, and poor prognosis of a variety of tumors including lung cancer [ 124 , 125 ], epithelial ovarian cancer [ 126 – 128 ], CRC [ 129 ], breast cancer [ 130 , 131 ], GC [ 132 ] and leiomyosarcoma [ 133 ].…”

A disulfidptosis-related classification and risk signature identifies immunotherapy biomarkers and predicts prognosis in gastric cancer: An observational study

“…Excitingly, a range of gene models centered around programmed cell death (such as ferroptosis and cuprotosis) have been developed. [21][22][23][24][31][32][33] These models hold great promise in identifying potential targets for therapeutic interventions in GC.…”

“…This area represents a new research direction in GC. [21][22][23][24] One novel and less explored mechanism of programmed cell death is the discovery of disulfidptosis this year. Researchers have observed that during glucose starvation, an abnormal accumulation of intracellular disulfide bonds disturbs the normal disulfide bonds between cytoskeletal proteins, ultimately leading to cell death.…”

Long non‑coding RNA lung cancer‑associated transcript 1 regulates ferroptosis via microRNA‑34a‑5p‑mediated GTP cyclohydrolase 1 downregulation in lung cancer cells

“…1E). Among the top 10 hub mRNAs in the PPI network displayed using STRING, jun proto-oncogene, C-X-C motif chemokine ligand 8, epiregulin, cyclin-dependent kinase inhibitor 1A, serpin family E member 1 and DNA damage-inducible transcript 3 were previously reported to be involved in ferroptosis (45)(46)(47)(48)(49)(50), suggesting that the remaining four genes, growth arrest and DNA damage inducible α (GADD45A), epithelial mitogen (EPGN), heparin-binding EGF-like growth factor (HBEGF) and transforming growth factor α (TGFA), may also have roles in ferroptosis (Fig. 1F and G).…”