Dataset Information

Construction of a Nomogram Based on a Hypoxia-Related lncRNA Signature to Improve the Prediction of Gastric Cancer Prognosis.

ABSTRACT: Background:Gastric cancer is one of the most common malignant tumors and has a poor prognosis. Hypoxia is related to the poor prognosis of cancer patients. We searched for hypoxia-related long non-coding RNAs (lncRNAs) to predict both overall survival (OS) and disease-free survival (DFS) of gastric cancer patients. Methods:We obtained hypoxia-related lncRNA expression profiles and clinical follow-up data of patients with gastric cancer from The Cancer Genome Atlas and the Molecular Signatures Database. The patients were randomly divided into a training group, test group and combined group. The hypoxia-related prognostic signature was constructed by Lasso regression and Cox regression models, the prognoses in different groups were compared by Kaplan-Meier (K-M) analysis, and the accuracy of the prognostic model was assessed by receiver operating characteristic (ROC) analysis. Results:A hypoxia-related prognostic signature comprising 10 lncRNAs was constructed to predict both OS and DFS in gastric cancer. In the training, test and combined groups, patients were divided into high- and low-risk groups according to the formula. Kaplan-Meier analysis showed that patients in the high-risk group have poor prognoses, and the difference was significant in the subgroup analyses. Receiver operating characteristic analysis revealed that the predictive power of the model prediction is more accurate than that of standard benchmarks. The signature differed across Helicobacter pylori (Hp) status and T stages. Multivariate Cox analysis showed that the signature is an independent risk factor for both OS and DFS. A clinically predictive nomogram combining the lncRNA signature and clinical features was constructed; the nomogram accurately predicted both OS and DFS and had high clinical application value. Weighted correlation network analysis combined with enrichment analysis showed that the primary pathways were the PI3K-Akt, JAK-STAT, and IL-17 signaling pathways. The target genes NOX4, COL8A1, and CHST1 were associated with poor prognosis in the Gene Expression Profiling Interactive Analysis, Gene Expression Omnibus, and K-M Plotter databases. Conclusions:Our 10-lncRNA prognostic signature and nomogram are accurate, reliable tools for predicting both OS and DFS in gastric cancer.

SUBMITTER: Chen Q

PROVIDER: S-EPMC7641644 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Construction of a Nomogram Based on a Hypoxia-Related lncRNA Signature to Improve the Prediction of Gastric Cancer Prognosis.

Chen Qian Q Hu Lang L Chen Kaihua K

Frontiers in genetics 20201020

<h4>Background</h4>Gastric cancer is one of the most common malignant tumors and has a poor prognosis. Hypoxia is related to the poor prognosis of cancer patients. We searched for hypoxia-related long non-coding RNAs (lncRNAs) to predict both overall survival (OS) and disease-free survival (DFS) of gastric cancer patients.<h4>Methods</h4>We obtained hypoxia-related lncRNA expression profiles and clinical follow-up data of patients with gastric cancer from The Cancer Genome Atlas and the Molecula ...[more]

PMID: 33193668

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Project description:BackgroundCuproptosis is a copper-triggered modality of mitochondrial cell death and cuproptosis process may play important roles in gastric cancer development. However, little is known about cuproptosis-related lncRNAs in gastric adenocarcinoma (STAD). This study is aimed to investigate the potential prognostic signatures of cuproptosis-related lncRNAs in STAD.MethodsThe Cancer Genome Atlas (TCGA) database were used to obtain gene expression profiles, clinicopathological, and OS information for STAD. Cuproptosis-related genes were collected based on previous studies and cuproptosis-related lncRNAs were screened out by co-expression analysis. The nomogram constructed by Cox regression analysis with the minimum absolute contraction and selection operator (lasso) algorithm. In addition, the potential response of ICB therapy and immune evasion incidence were estimated with Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Immune checkpoint expressions associated with risk scores were also analyzed. The correlation of immune checkpoint CD209 and HAVCR2 expressions associated with risk scores were experimentally testified by RT-qPCR, Western Blot, and IHC.ResultsPatients were classified into high-risk and low-risk groups based on the risk score calculated in this model. The Kaplan-Meier survival curve analysis revealed that the high-risk group was associated with poor prognosis. Multivariate Cox regression analysis suggested that this lncRNA prediction model was an independent risk factor affecting the OS rate. Furthermore, ROC curve indicates that the nomogram was superior to traditional clinicopathological features in predicting STAD prognosis. Finally, functional enrichment analysis and immune checkpoint investigation revealed that the nomogram is notably associated with cholesterol metabolism and immune functions, RT-qPCR and Western Blotting demonstrated the co-expression relationship of LINC01150 with CD209 and HAVCR2.ConclusionA novel cuproptosis-related lncRNAs signature impacts on the prognosis and immunological features of GC.

Project description:Background: Glycolysis is closely related to the occurrence and progression of gastric cancer (GC). Currently, there is no systematic study on using the glycolysis-related long non-coding RNA (lncRNA) as a model for predicting the survival time in patients with GC. Therefore, it was essential to develop a signature for predicting the survival based on glycolysis-related lncRNA in patients with GC. Materials and methods: LncRNA expression profiles, containing 375 stomach adenocarcinoma (STAD) samples, were obtained from The Cancer Genome Atlas (TCGA) database. The co-expression network of lncRNA and glycolysis-related genes was used to identify the glycolysis-related lncRNAs. The Kaplan-Meier survival analysis and univariate Cox regression analysis were used to detect the glycolysis-related lncRNA with prognostic significance. Then, Bayesian Lasso-logistic and multivariate Cox regression analyses were performed to screen the glycolysis-related lncRNA with independent prognostic significance and to develop the risk model. Patients were assigned into the low- and high-risk cohorts according to their risk scores. A nomogram model was constructed based on clinical information and risk scores. Gene Set Enrichment Analysis (GSEA) was performed to visualize the functional and pathway enrichment analyses of the glycolysis-related lncRNA. Finally, the robustness of the results obtained was verified in an internal validation data set. Results: Seven glycolysis-related lncRNAs (AL353804.1, AC010719.1, TNFRSF10A-AS1, AC005586.1, AL355574.1, AC009948.1, and AL161785.1) were obtained to construct a risk model for prognosis prediction in the STAD patients using Lasso regression and multivariate Cox regression analyses. The risk score was identified as an independent prognostic factor for the patients with STAD [HR = 1.315, 95% CI (1.056-1.130), p < 0.001] via multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were drawn and the area under curve (AUC) values of 1-, 3-, and 5-year overall survival (OS) were calculated to be 0.691, 0.717, and 0.723 respectively. Similar results were obtained in the validation data set. In addition, seven glycolysis-related lncRNAs were significantly enriched in the classical tumor processes and pathways including cell adhesion, positive regulation of vascular endothelial growth factor, leukocyte transendothelial migration, and JAK_STAT signaling pathway. Conclusion: The prognostic prediction model constructed using seven glycolysis-related lncRNA could be used to predict the prognosis in patients with STAD, which might help clinicians in the clinical treatment for STAD.

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Project description:IntroductionBreast cancer is the most common form of cancer worldwide and a serious threat to women. Hypoxia is thought to be associated with poor prognosis of patients with cancer. Long non-coding RNAs are differentially expressed during tumorigenesis and can serve as unambiguous molecular biomarkers for the prognosis of breast cancer.MethodsHere, we accessed the data from The Cancer Genome Atlas for model construction and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to identify biological functions. Four prognostic hypoxia-related lncRNAs identified by univariate, LASSO, and multivariate Cox regression analyses were used to develop a prognostic risk-related signature. Kaplan-Meier and receiver operating characteristic curve analyses were performed, and independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate the specificity and sensitivity of the signature. Survival analysis and receiver operating characteristic curve analyses of the validation cohort were operated to corroborate the robustness of the model.ResultsOur results demonstrate the development of a reliable prognostic gene signature comprising four long non-coding RNAs (AL031316.1, AC004585.1, LINC01235, and ACTA2-AS1). The signature displayed irreplaceable prognostic power for overall survival in patients with breast cancer in both the training and validation cohorts. Furthermore, immune cell infiltration analysis revealed that B cells, CD4 T cells, CD8 T cells, neutrophils, and dendritic cells were significantly different between the high-risk and low-risk groups. The high-risk and low-risk groups could be precisely distinguished using the risk signature to predict patient outcomes.DiscussionIn summary, our study proves that hypoxia-related long non-coding RNAs serve as accurate indicators of poor prognosis and short overall survival, and are likely to act as potential targets for future cancer therapy.

Dataset Information

Construction of a Nomogram Based on a Hypoxia-Related lncRNA Signature to Improve the Prediction of Gastric Cancer Prognosis.

Publications

Construction of a Nomogram Based on a Hypoxia-Related lncRNA Signature to Improve the Prediction of Gastric Cancer Prognosis.

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