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Pathway- and clinical-factor-based risk model predicts the prognosis of patients with gastric cancer.

ABSTRACT: Gastric cancer (GC) has a high incidence and mortality rate. If discovered late, GC tends to have a poor prognosis. Improvements in the prognostic accuracy of GC through combined analysis of multiple relevant genes and clinical factors may solve this problem. In the present study, GSE62254 (including 300 GC tissues), obtained from the Gene Expression Omnibus database, was used as a training set, and the mRNA?sequencing data of GC (including 384 GC tissues) downloaded from the Cancer Genome Atlas database served as a validation set. Based on the t?test and Wilcoxon test, the significantly differentially expressed genes (DEGs) were obtained by screening the intersecting DEGs. The prognosis-associated genes and clinical factors were identified using Cox regression analysis in the R survival package. The optimal prognosis?associated pathways were examined using the Cox?proportional hazards (Cox?PH) model in the R penalized package. Finally, risk prediction models were constructed and validated using the Cox?PH model and the Kaplan?Meier method, respectively. There were a total of 382 significant DEGs, including 268 upregulated genes and 114 downregulated genes. A total of 50 prognosis?associated genes were identified, 16 optimal prognosis?associated pathways (including mitochondrial pathway and the tyrosine?protein kinase JAK?signal transducer and activator of transcription signaling pathway, which involve caspase 7, phosphoinositide?3?kinase regulatory subunit 3, peroxisome proliferator?activated receptor ? and collagen triple helix repeat containing 1) and four prognosis?associated clinical factors [including Pathologic_N, Pathologic_stage, mutL homolog 1 (MLH1) mutation and recurrence]. The pathway? and clinical?factor?based risk prediction model exhibited marked prognostic accuracy. The clinical?factor?based risk prediction model with improved P?values for prognosis prediction may be superior to the pathway?based risk prediction model in predicting the prognosis of GC patients.

SUBMITTER: Yang J

PROVIDER: S-EPMC5928624 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Pathway- and clinical-factor-based risk model predicts the prognosis of patients with gastric cancer.

Yang Junchi J Bo Lumin L Han Ting T Ding Dan D Nie Mingming M Yin Kai K

Molecular medicine reports 20180309 5

Gastric cancer (GC) has a high incidence and mortality rate. If discovered late, GC tends to have a poor prognosis. Improvements in the prognostic accuracy of GC through combined analysis of multiple relevant genes and clinical factors may solve this problem. In the present study, GSE62254 (including 300 GC tissues), obtained from the Gene Expression Omnibus database, was used as a training set, and the mRNA‑sequencing data of GC (including 384 GC tissues) downloaded from the Cancer Genome Atlas ...[more]

PMID: 29532879

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Project description:Background: Autophagy plays a vital role in cancer initiation, malignant progression, and resistance to treatment. However, autophagy-related genes (ARGs) have rarely been analyzed in gastric cancer (GC). The purpose of this study was to analyze ARGs in GC using bioinformatic analysis and to identify new biomarkers for predicting the overall survival (OS) of patients with GC. Methods: The gene expression profiles and clinical data of patients with GC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and ARGs were obtained from two other datasets (the Human Autophagy Database and Molecular Signatures Database). Lasso, univariate, and multivariate Cox regression analyses were performed to identify the OS-related ARGs. Finally, a six-ARG model was identified as a prognostic indicator using the risk-score model, and survival and prognostic performance were analyzed based on the Kaplan-Meier test and ROC curve. Estimate calculations were used to assess the immune status of this model, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed for investigating the functions and terms associated with the model-related genes in GC. Results: The six ARGs, DYNLL1, PGK2, HPR, PLOD2, PHYHIP, and CXCR4, were identified using Lasso and Cox regression analyses. Survival analysis revealed that the OS of GC patients in the high-risk group was significantly lower than that of the low-risk group (p < 0.05). The ROC curves revealed that the risk score model exhibited better prognostic performance with respect to OS. Multivariate Cox regression analysis indicated that the model was an independent predictor of OS and was not affected by most of the clinical traits (p < 0.05). The model-related genes were associated with immune suppression and several biological process terms, such as extracellular structure organization and matrix organization. Moreover, the genes were associated with the P13K-Akt signaling pathway, focal adhesion, and MAPK signaling pathway. Conclusions: This study presents potential prognostic biomarkers for GC patients that would aid in determining the best patient-specific course of treatment.

Project description:BackgroundThe prognosis of gastric cancer is difficult to determine, although clinical indicators provide valuable evidence.MethodsIn this study, using screened biomarkers of gastric cancer in combination with random forest variable hunting and multivariable Cox regression, a risk score model was developed to predict the survival of gastric cancer. Survival difference between high/low-risk groups were compared. The relationship between risk score and other clinicopathological indicators was evaluated. Gene set enrichment analysis (GSEA) was used to identify pathways associated with risk scores.ResultsThe patients with high risk scores (median overall survival: 20.2 months, 95% CI [16.9-26.0] months) tend to exhibit early events compared with those with low risk scores (median survival: 70.0 months, 95% CI [46.9-101] months, p = 1.80e-5). Further validation was implemented in another three independent datasets (GSE15459, GSE26253, GSE62254). Correlation analyses between clinical observations and risk scores were performed, and the results indicated that the risk score was not significantly associated with gender, age and primary tumor size but was significantly associated with grade and tumor stage. In addition, the risk score was also not influenced by radiation therapy. Cox multivariate regression and three-year survival nomogram suggest that the risk score is an important indicator of gastric cancer prognosis. GSEA was used to identified KEGG pathways significantly associated with risk score, and signaling pathways involved in focal adhesion and the TGF-beta signaling pathway were identified.ConclusionThe risk score model successfully predicted the survival of 1,294 gastric cancer samples from four independent datasets and is among the most important indicators in clinical clinicopathological information for the prognosis of gastric cancer. To our knowledge, it is the first report to predict the survival of gastric cancer using optimized expression panel.

Project description:Sorafenib is used to treat digestive system tumors in patients who do not respond to or cannot tolerate surgery. However, the roles and inhibitory mechanisms of sorafenib against hepatocellular carcinoma (HCC) are unclear. Differentially expressed genes in tissues from responders and nonresponders to sorafenib were investigated using the HCC GSE109211 data set. Biological functions and mechanisms were studied using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. The expression levels of differential expressed target genes were identified in HCC tissues, using The Cancer Genome Atlas database, and their prognostic and diagnostic values were explored using survival and receiver operating characteristic curve analysis. A nomogram and risk model of sorafenib-response target genes enabled the evaluation of the prognosis of patients with HCC. The relationship between risk scores and levels of infiltrating immune cells was visualized via correlation analysis. We identified 1620 sorafenib-response target genes involved in the PPAR signaling pathway, antigen processing and presentation, and ferroptosis. SLC41A3, SEC61A1, LRP4, PPM1G, and HSP90AA1 were independent risk factors for a poor prognosis for patients with HCC and had diagnostic value. A risk model based on SLC41A3, SEC61A1, LRP4, PPM1G, and HSP90AA1 expression showed that patients with HCC in the high-risk group had a worse prognosis. Consensus-clustering analysis (performed with K set to 2) distinguished two clusters (the cluster 1 and cluster 2 groups). Patients in cluster 1 survived significantly longer than those in cluster 2. The risk score correlated with the levels of T cells, cytotoxic lymphocytes, CD8+ T cells, macrophages, memory B cells, follicular helper T cells, and other immune cells. The high risk based on the sorafenib-response targets SLC41A3, SEC61A1, LRP4, PPM1G, and HSP90AA1 represented the poor prognosis for patients with HCC and significantly correlated with the levels of immune infiltrating cells in HCC.

Project description:BACKGROUND:Accurate prediction of the prognosis of RCC using a single biomarker is challenging due to the genetic heterogeneity of the disease. However, it is essential to develop an accurate system to allow better patient selection for optimal treatment strategies. ARL4C, ECT2, SOD2, and STEAP3 are novel molecular biomarkers identified in earlier studies as survival-related genes by comprehensive analyses of 43 primary RCC tissues and RCC cell lines. METHODS:To develop a prognostic model based on these multiple biomarkers, the expression of four biomarkers ARL4C, ECT2, SOD2, and STEAP3 in primary RCC tissue were semi-quantitatively investigated by immunohistochemical analysis in an independent cohort of 97 patients who underwent nephrectomy, and the clinical significance of these biomarkers were analyzed by survival analysis using Kaplan-Meier curves. The prognostic model was constructed by calculation of the contribution score to prognosis of each biomarker on Cox regression analysis, and its prognostic performance was validated. RESULTS:Patients whose tumors had high expression of the individual biomarkers had shorter cancer-specific survival (CSS) from the time of primary nephrectomy. The prognostic model based on four biomarkers segregated the patients into a high- and low-risk scored group according to defined cut-off value. This approach was more robust in predicting CSS compared to each single biomarker alone in the total of 97 patients with RCC. Especially in the 36 metastatic RCC patients, our prognostic model could more accurately predict early events within 2 years of diagnosis of metastasis. In addition, high risk-scored patients with particular strong SOD2 expression had a much worse prognosis in 25 patients with metastatic RCC who were treated with molecular targeting agents. CONCLUSIONS:Our findings indicate that a prognostic model based on four novel biomarkers provides valuable data for prediction of clinical prognosis and useful information for considering the follow-up conditions and therapeutic strategies for patients with primary and metastatic RCC.

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Dataset Information

Pathway- and clinical-factor-based risk model predicts the prognosis of patients with gastric cancer.

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Pathway- and clinical-factor-based risk model predicts the prognosis of patients with gastric cancer.

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