- Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer. - In this study, we attempted to construct a novel metabolism-related gene (MRG) signature for predicting the survival probability of BC patients.. - Results: In the present study, 27 differentially expressed MRGs were identified, most of which presented mutations in BC patients, and LRP1 showed the highest mutation rate. - Furthermore, survival analysis indicated that BC patients in the high-risk group had a dramatically lower survival probability than those in the low-risk group.. - The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. - If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. - Page 2 of 14 Li et al. - On the other hand, the energy demand for meeting the survival of cancer cells in the nutrient-deprived tumor microenvironment relies on metabolic alterations [12].. - FOXJ1 also has a role in the glycolytic phenotype of BC [18]. - Furthermore, the epigenetic perturbation of SAT1 and ASS1 may be involved in the chemotherapy and personalized ther- apy of BC by regulating its amino acid metabolism [19].. - Hence, metabolism plays a vital role in the occurrence and development of BC, and research focusing on metab- olism-related genes (MRGs) may contribute to further understanding the role of metabolism in BC and identify- ing novel therapeutic targets.. - For instance, Wen et al. - Moreover, a risk model with good performance in the prognostic prediction of hepatocellular carcinoma patients was built based on energy metabolism genes [21]. - Wu et al. - Moreover, we fur- ther validated the mRNA expression level of genes in the MRG signature through real-time PCR. - Moreo- ver, two volcano plots were plotted using the ‘ggplot2’ R package to visualize DEGs in the TCGA and GEO data- bases [24]. - Finally, the metabolism-related DEGs were identified by overlapping the MRGs, DEGs in TCGA and DEGs in GEO using the ‘VennDiagram’ R package [25].. - Therefore, the ‘clusterProfiler’ R package was utilized to conduct GO functional annotation and KEGG pathway enrichment analysis for the metabolism-related DEGs [26], and a P value <. - To further investigate the role of metabolism-related DEGs in BC, the ‘maftools’ R package was used to ana- lyze the mutation frequency and mutation type of BC. - First, univariate Cox regression analysis was performed using the ‘survival’ R package to screen the prognosis-related MRGs from the metabolism- related DEGs in the training set, with the a cutoff value of P <. - Then, prognosis-related MRGs were submit- ted to multivariate Cox regression analysis to construct an optimal prognostic MRG signature in the training set by the ‘survival’ R package. - Thus, BC patients in the training set, testing set and vali- dation set were stratified into the high-risk and low-risk groups based on the median risk score value of the MRG signature. - Moreover, the Kaplan-Meier (KM) survival curves were drawn by the ‘survminer’ R package to reveal the OS for patients in the high-risk and low-risk groups, and the log-rank test was used to analyze significant dif- ferences in OS. - The association between the MRG signature and clinico- pathological features, including gender, age, pathological tumor stage, pathological T stage, pathological M stage, pathological N stage, was calculated by t test in the train- ing set, and P <. - In addition, a cluster heatmap was drawn to show the distribution trends of gender, age, pathological tumor stage, pathological T stage, pathological M stage, and pathological N stage between the low-risk and high-risk groups in the training set.. - The MRG signature and clinicopathological features were used to identify independent prognostic factors with uni- variate and multivariate Cox regression analyses in the training set, and the results of univariate and multivari- ate Cox regression analyses are by forest plots. - To further analyze the roles of genes in the MRG signa- ture, we first examined the expression levels of genes in the MRG signature in the TCGA and GEO databases.. - Next, we collected 10 cancer tissues and 10 pericarcino- matous tissues from BC patients in The Second Affiliated Hospital of Kunming Medical University. - 0.05, 1166 DEGs, including 288 upregulated genes and 878 down- regulated genes, were identified between normal and BC samples in the GEO database (Fig. - Page 4 of 14 Li et al. - Finally, 27 metabolism- related DEGs were identified, including 19 downregu- lated and 8 upregulated genes, by overlapping the genes among MRGs, DEGs in GSE13507 and DEGs in the TCGA database (Fig. - For CC analysis, the 8 upregulated metabolism-related DEGs were significantly Fig. - 1 Identification of Metabolism-related DEGs. - involved in the mitochondrial matrix, mitochondrial inner membrane, and nucleoid (Fig. - In addition, for MF, the 8 upregulated metabolism-related DEGs were not significantly enriched. - For CC and MF analysis, the 19 downregu- lated metabolism-related DEGs were not significantly enriched. - To further investigate the roles of 27 metabolism-related DEGs in the BC, we further analyzed the landscape of somatic mutations for 27 metabolism-related DEGs using somatic mutation data of 414 BC samples from the TCGA database. - A The enriched biological processes by metabolism-related DEGs. - B The enriched cellular components by metabolism-related DEGs. - C The enriched molecular functions by metabolism-related DEGs. - D The enriched KEGG pathways by metabolism-related DEGs. - Page 6 of 14 Li et al. - These results further revealed that metabolism-related genes might play key roles in BC.. - 4A), in the training set. - Next, 3 genes, MAOB, FASN and LRP1, were reserved to estab- lish a prognostic MRG signature based on the multivari- ate Cox regression analysis in the training set (Fig. - Thus, we further plotted the KM sur- vival curves of MAOB, FASN and LRP1 in the train- ing, testing and validation sets and found that patients in the high expression group showed a worse progno- sis than patients in the low expression group (Fig. - 3 PPI network genetic variation for metabolism-related DEGs. - A The PPI network of 27 metabolism-related DEGs. - C The mutation frequency of 23 metabolism-related DEGs in 414 BC samples from the TCGA database. - The patients in the training set were stratified into a high-risk group and a low-risk group based on the median risk score value. - 5A, patients in the high risk group showed sig- nificantly poorer OS than those in the low-risk group.. - Consistently, the patients in the high-risk group appeared to have a higher mortality than patients in the low-risk group (Fig. - 4 Identification of prognostic metabolism-related DEGs. - A Univariate Cox regression analysis identified 5 prognostic metabolism-related DEGs.. - B Multivariate Cox regression analysis reserved 3 prognostic metabolism-related DEGs for establishing the prognostic MRG signature. - Page 8 of 14 Li et al. - Furthermore, based on the formula mentioned above, the patients in the testing set and validation set were stratified into a high-risk group and a low-risk group according to the median risk score value, respectively. - Consistent with the results of the training set, the patients in the high-risk group also pre- sented significantly worse OS than those in the low-risk. - 5H and I), and the AUC values in the testing set were 0.637 at 1 year, 0.680 at 3 years and 0.631 at 5 years (Fig. - Meanwhile, those in the validation set were and 0.753, Fig. - 5 Assessing the efficiencies of the prognostic MRG signature in the training set and validation set. - D E F The distribution of risk scores and the survival status of patients in the training set (D), the testing set (E) and the validation set (F), and each dot represents a BC patient. - To explore the role of the MRG signature in the progres- sion of BC, the association between the MRG signature and clinicopathological characteristics was investigated in the training set. - As shown in Table 1, the patients in the high-risk group were inclined to include more patients older than 60. - Moreover, the patients in the high-risk group appeared to contain more high-grade BC (including pathological tumor stage 3 and 4) (Table 1).. - First, univariate and multivariate Cox regression analyses were per- formed to screen independent prognostic factors in the training set. - Notably, the expression levels of both MAOB and LRP1 were downregulated in tumor sam- ples compared with normal samples in the TCGA and GEO databases (Fig. - Increasing evidence has revealed that metabolic imbalance can influence the growth, pro- liferation, angiogenesis, and invasion of cancer cells Table 1 Clinicopathological characteristics of patients in high- and low-risk group in the training set. - Page 10 of 14 Li et al. - Hence, the present study aimed to systematically investigate the role of MRGs in the occurrence and pro- gression of BC and screen biomarkers for predicting the OS of BC.. - In the present study, we first identified 27 differen- tially expressed MRGs by overlapping the MRGs, DEGs in TCGA, and DEGs in GEO (Fig. - In the present study, we found that higher expression of MAOB was associated with worse survival in BC patients (Fig. - 6 Construction of a nomogram for better predicting the 1-year, 3-year and 5-year OS of patients in the training set. - C Nomogram based on the age, pathological tumor stage and risk score was established in the training set. - D The calibration curve showed the predictive efficiency of nomogram in the training set. - Page 12 of 14 Li et al. - Thus, more studies are needed to clarify the role of MAOB in the occurrence and development of BC.. - Moreover, the expression of FASN is involved in the progression of BC [54]. - Thus, our study further highlights the role of FASN in the occurrence and development of BC.. - In addition, LRP1 mutation plays a key role in the occur- rence of gastric cancer [59]. - More importantly, the expression of LRP1 is involved in the outcome of lung adenocarcinoma [61].. - MRG: Metabolism-related gene. - The KM survival curves of MAOB, FASN and LRP1 in the training, testing and validation sets. - Twenty-seven metabolism-related DEGs.. - The survival analysis between mutated and nonmutated samples of each mutated gene among the 27 metabolism- related DEGs.. - Yinglong Huang, and Ting Luan participated in the data analysis and determi- nation of analytical method. - The GSE13507 dataset used in the present study can be found in GEO data- base (https. - Non-coding RNAs in the reprogramming of glucose metabolism in cancer. - Putluri N, Shojaie A, Vasu VT, Vareed SK, Nalluri S, Putluri V, et al. - Yang X, Cheng Y, Li P, Tao J, Deng X, Zhang X, et al. - Identification and prognostic value of metabolism-related genes in gastric cancer. - Page 14 of 14 Li et al. - Soukup V, Čapoun O, Cohen D, Hernández V, Babjuk M, Burger M, et al.. - Balar AV, Galsky MD, Rosenberg JE, Powles T, Petrylak DP, Bellmunt J, et al. - Cheng Y, Yang X, Deng X, Zhang X, Li P, Tao J, et al. - Weng L, Shen S, Wu S, Yin X, Liu B, Shang M, et al. - Differential expression of prosta- glandin I2 synthase associated with Arachidonic acid pathway in the Oral squamous cell carcinoma. - Genomic characterization of non-invasive differentiated-type gastric Cancer in the Japanese population. - 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