ObjectiveTo onstructe a prognostic model for gastric cancer based on disulfidoptosis-related genes. MethodsFirstly, transcriptome data and clinical data were obtained from the TCGA and GEO databases to explore the expression of disulfidoptosis-related genes in gastric cancer tissues and normal tissues, as well as their impact on the overall survival (OS) of gastric cancer patients. Subsequently, two clusters of disulfidoptosis-related gene were determined by consensus clustering, key genes were further selected by using LASSO regression, and a multivariate Cox proportional hazards regression model was constructed to predict OS. ResultsAmong the 24 kinds of disulfidoptosis-associated genes, 16 exhibited statistically significant differences in expression between gastric cancer tissues and normal tissues (P<0.05), and results of univariate Cox proportional hazards regression model showed that 9 kinds of disulfidoptosis-associated genes were associated with OS (P<0.05). The 24 kinds of disulfidoptosis-associated genes were grouped into 2 clusters by using the consensus clustering algorithm, with 299 differentially expressed genes between the two clusters. In the training set, 14 genes were determined by using LASSO regression to construct the OS prediction model, and risk scores were calculated. The OS of the high-risk group was significantly worse than that of the low-risk group (P<0.05), and this prediction model also had a high area under the curve value in the validation set. ConclusionsThe OS prediction model based on disulfidoptosis-associated genes can predict the prognosis of gastric cancer patients.
ObjectiveTo investigate the relation between disulfidptosis-related genes (DRGs) and prognosis or immunotherapy response of patients with pancreatic cancer (PC). MethodsThe transcriptome data, somatic mutation data, and corresponding clinical information of the patients with PC in The Cancer Genome Atlas (TCGA) were downloaded. The DRGs mutated in the PC were screened out from the 15 known DRGs. The DRGs subtypes were identified by consensus clustering algorithm, and then the relation between the identified DRGs subtypes and the prognosis of patients with PC, immune cell infiltration or functional enrichment pathway was analyzed. Further, a risk score was calculated according to the DRGs gene expression level, and the patients were categorized into high-risk and low-risk groups based on the mean value of the risk score. The risk score and overall survival of the patients with high-risk and low-risk were compared. Finally, the relation between the risk score and (or) tumor mutation burden (TMB) and the prognosis of patients with PC was assessed. ResultsThe transcriptome data and corresponding clinical information of the 177 patients with PC were downloaded from TCGA, including 161 patients with somatic mutation data. A total of 10 mutated DRGs were screened out. Two DRGs subtypes were identified, namely subtype A and subtype B. The overall survival of PC patients with subtype A was better than that of patients with subtype B (χ2=8.316, P=0.003). The abundance of immune cell infiltration in the PC patients with subtype A was higher and mainly enriched in the metabolic and conduction related pathways as compaired with the patients with subtype B. The mean risk score of 177 patients with PC was 1.921, including 157 cases in the high-risk group and 20 cases in the low-risk group. The risk score of patients with subtype B was higher than that of patients with subtype A (t=14.031, P<0.001). The overall survival of the low-risk group was better than that of the high-risk group (χ2=17.058, P<0.001), and the TMB value of the PC patients with high-risk was higher than that of the PC patients with low-risk (t=5.642, P=0.014). The mean TMB of 161 patients with somatic mutation data was 2.767, including 128 cases in the high-TMB group and 33 cases in the low-TMB group. The overall survival of patients in the high-TMB group was worse than that of patients in the low-TMB group (χ2=7.425, P=0.006). ConclusionDRGs are closely related to the prognosis and immunotherapy response of patients with PC, and targeted treatment of DRGs might potentially provide a new idea for the diagnosis and treatment of PC.