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Establishment and verification of prognostic model for gastric cancer based on autophagy-related genes.

ABSTRACT: Autophagy played a significant role in the development of cancer. In this study, we explored the value of autophagy-associated genes in gastric cancer. RNA sequencing and clinical information containing 375 gastric cancer and 32 normal tissues were gathered from the TCGA portal. Then we stochastically allocated the autophagy-associated genes (AAGs) to training and testing groups. Next, we screened the discrepantly expressed AAGs and the prognostic AAGs by Cox regression analysis and Lasso regression analysis. Afterwards, we structured the model by using the prognostic AAGs and plotted Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves to verify the performance of models in both groups. Besides, we utilized Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to explore the molecular mechanisms of AAGs in gastric cancer. Finally, we demonstrated discrepant expression of AAGs within gastric cancer and non-tumor tissues at protein level with immunohistochemistry. 28 discrepantly expressed AAGs were screened from the TCGA database which contained 375 gastric cancer and 32 non-tumor samples. Cox and Lasso regression analyses were performed in training group and then we got 5 prognostic AAGs to establish the prognostic model. The patients who had high risk possessed worse overall survival (OS) both in training group (5-year OS, 47.6% vs 23.1%; P < 0.0001) and test group (5-year OS, 49.2% vs 0%, P=0.019). The proportion under ROC curves (AUC) were significant both in training group and test group (5-year AUC, 0.736 vs 0.809). Through this study, we constructed a model for gastric cancer patients which may provide individual treatment and superior prognosis.

SUBMITTER: Chen L

PROVIDER: S-EPMC8085875 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAccumulating evidence implies that autophagy plays a critical role in breast cancer development and progression. It is crucial to screen out autophagy-related encoding genes (ARGs) with prognostic value in breast cancer and reveal their biological properties in the aggressiveness of breast cancer.MethodsUnivariate and multivariate Cox proportional hazards analyses were used to identify a prognostic risk model of ARGs from The Cancer Genome Atlas (TCGA). Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and receiver operating characteristic (ROC) curve analysis were performed to validate the risk model. Western blot and immunohistochemistry (IHC) were conducted to assess the expression of VPS35 (one of ARGs in risk model). CCK8, Colony formation assay, Transwell migration/invasion assays and autophagy flux assay were used to confirm biological function of VPS35 in breast cancer.ResultsIn this study, the prognostic risk model consisting of six ARGs (VPS35, TRIM21, PRKAB2, RUFY4, MAP1LC3A and LARP1) in breast cancer were identified. The risk model was further verified as a novel independent prognostic factor for breast cancer patients. We also clarified that vacuolar protein sorting-associated protein 35 (VPS35), one of ARGs in the risk model, was upregulated in breast cancer samples and cell lines. VPS35 overexpression was correlated with more aggressive phenotype of breast cancer and indicated worse prognosis in both progression-free survival and overall survival analyses. Meanwhile, VPS35 knockdown inhibited breast cancer cell proliferation, migration and invasion, suggesting that VPS35 promoted the progression of breast cancer. VPS35 silence also influenced autophagy process, indicating that VPS35 was essential for autophagy completion.ConclusionTaken together, the six ARGs risk model has a remarkably prognostic value for breast cancer. Among them, VPS35 might exert as a significant oncogenic and prognostic factor for breast cancer and could be a promising autophagy-related therapeutic target in clinical practice.

Project description:Gastric cancer (GC) is one of the most common tumors in the world, and apoptosis is closely associated with GC. A number of therapeutic methods have been implemented to increase the survival in GC patients, but the outcomes remain unsatisfactory. Apoptosis is a highly conserved form of cell death, but aberrant regulation of the process also leads to a variety of major human diseases. As variations of apoptotic genes may increase susceptibility to gastric cancer. Thus, it is critical to identify novel and potent tools to predict the overall survival (OS) and treatment efficacy of GC. The expression profiles and clinical characteristics of TCGA-STAD and GSE15459 cohorts were downloaded from TCGA and GEO. Apoptotic genes were extracted from the GeneCards database. Apoptosis risk scores were constructed by combining Cox regression and LASSO regression. The GSE15459 and TCGA internal validation sets were used for external validation. Moreover, we explored the relationship between the apoptosis risk score and clinical characteristics, drug sensitivity, tumor microenvironment (TME) and tumor mutational burden (TMB). Finally, we used GSVA to further explore the signaling pathways associated with apoptosis risk. By performing TCGA-STAD differential analysis, we obtained 839 differentially expressed genes, which were then analyzed by Cox regressions and LASSO regression to establish 23 genes associated with apoptosis risk scores. We used the test validation cohort from TCGA-STAD and the GSE15459 dataset for external validation. The AUC values of the ROC curve for 2-, 3-, and 5-years survival were 0.7, 0.71, and 0.71 in the internal validation cohort from TCGA-STAD and 0.77, 0.74, and 0.75 in the GSE15459 dataset, respectively. We constructed a nomogram by combining the apoptosis risk signature and some clinical characteristics from TCGA-STAD. Analysis of apoptosis risk scores and clinical characteristics demonstrated notable differences in apoptosis risk scores between survival status, sex, grade, stage, and T stage. Finally, the apoptosis risk score was correlated with TME characteristics, drug sensitivity, TMB, and TIDE scores.

Project description:PurposeAutophagy-related proteins (ATG) play a crucial role in autophagy. Recently, the functions of autophagy in cancer have been gathering attention. However, the prognostic value of ATGs in gastric cancer (GC) has not been explored.MethodsThe Kaplan-Meier plotter (KM plotter) online database was used to examine the value of ATGs gene expression levels in overall survival (OS) prediction in GC patients with different clinical stage, differentiation, gender, HER2 status, and therapeutic strategy. In vitro experiments applied VE-822, an effective GC treatment, to assess cell migration and proliferation in gastric mucosa epithelial cells, and real-time PCR was used to measure alterations of autophagy-related gene expression.ResultsHigh ATG3, ATG4C, ATG5, and ATG10 mRNA levels were associated with good OS, while increased ATG4B, ATG7, ATG12, ATG16L1, and TECPR1 mRNA levels related to unfavorable OS in patients with GC. ATG12 overexpression had different effects on OS due to high levels of heterogeneity. High ATG12 expression was correlated with good OS in female patients with GC and with bad OS for male patients. Additionally, the increased ATG12 expression was more likely to get a satisfactory OS in patients who underwent surgery alone but was associated with poor OS for patients treated with 5-FU adjuvant. In addition, elevated TECPR1 expression was related to favorable OS for patients with poorly differentiated type, while for patients with moderate differentiation, it was relevant to poor OS. The in vitro experiments showed that berzosertib could significantly inhibit the migration and proliferation of human gastric mucosa epithelial cells, and further real-time PCR assessment of ATG expressions partially coincided with the bioinformation analysis above.ConclusionThese results indicate that individual ATGs have unique prognostic significance interpreted using Kaplan-Meier plotter analysis and in vitro experiments, and this may help guide clinical therapeutic strategy and promote OS by individualizing therapy for GC patients.

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Establishment and verification of prognostic model for gastric cancer based on autophagy-related genes.

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