Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features
BackgroundThis study aimed to construct a prognostic stratification system for gastric cancer (GC) using tumour invasion-related genes to more accurately predict the clinical prognosis of GC.MethodologyTumour invasion-related genes were downloaded from CancerSEA, and their expression data in the TCG...
| Main Authors: | , , , , , |
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| Format: | Article |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.848163/full |
| _version_ | 1811336214815440896 |
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| author | Haonan Guo Hui Tang Yang Zhao Qianwen Zhao Xianliang Hou Lei Ren |
| author_facet | Haonan Guo Hui Tang Yang Zhao Qianwen Zhao Xianliang Hou Lei Ren |
| author_sort | Haonan Guo |
| collection | DOAJ |
| description | BackgroundThis study aimed to construct a prognostic stratification system for gastric cancer (GC) using tumour invasion-related genes to more accurately predict the clinical prognosis of GC.MethodologyTumour invasion-related genes were downloaded from CancerSEA, and their expression data in the TCGA-STAD dataset were used to cluster samples via non-negative matrix factorisation (NMF). Differentially expressed genes (DEGs) between subtypes were identified using the limma package. KEGG pathway and GO functional enrichment analyses were conducted using the WebGestaltR package (v0.4.2). The immune scores of molecular subtypes were evaluated using the R package ESTIMATE, MCPcounter and the ssGSEA function of the GSVA package. Univariate, multivariate and lasso regression analyses of DEGs were performed using the coxph function of the survival package and the glmnet package to construct a RiskScore model. The robustness of the model was validated using internal and external datasets, and a nomogram was constructed based on the model.ResultsBased on 97 tumour invasion-related genes, 353 GC samples from TCGA were categorised into two subtypes, thereby indicating the presence of inter-subtype differences in prognosis. A total of 569 DEGs were identified between the two subtypes; of which, four genes were selected to construct the risk model. This four-gene signature was robust and exhibited stable predictive performance in different platform datasets (GSE26942 and GSE66229), indicating that the established model performed better than other existing models.ConclusionA prognostic stratification system based on a four-gene signature was developed with a desirable area under the curve in the training and independent validation sets. Therefore, the use of this system as a molecular diagnostic test is recommended to assess the prognostic risk of patients with GC. |
| first_indexed | 2024-04-13T17:36:15Z |
| format | Article |
| id | doaj.art-8f950d87e9cf49fab2658166f2459c96 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-13T17:36:15Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-8f950d87e9cf49fab2658166f2459c962022-12-22T02:37:22ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-06-011210.3389/fonc.2022.848163848163Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related FeaturesHaonan Guo0Hui Tang1Yang Zhao2Qianwen Zhao3Xianliang Hou4Lei Ren5Department of Clinical Laboratory, The Affiliated Hospital of Guilin Medical University, Guilin, ChinaDepartment of Clinical Laboratory, The Affiliated Hospital of Guilin Medical University, Guilin, ChinaDepartment of Human Resources, The Affiliated Hospital of Guilin Medical University, Guilin, ChinaDepartment of Clinical Laboratory, The Affiliated Hospital of Guilin Medical University, Guilin, ChinaCentral Laboratory, Guangxi Health Commission Key Laboratory of Glucose and Lipid Metabolism Disorders, The Second Affiliated Hospital of Guilin Medical University, Guilin, ChinaDepartment of Clinical Laboratory, The Affiliated Hospital of Guilin Medical University, Guilin, ChinaBackgroundThis study aimed to construct a prognostic stratification system for gastric cancer (GC) using tumour invasion-related genes to more accurately predict the clinical prognosis of GC.MethodologyTumour invasion-related genes were downloaded from CancerSEA, and their expression data in the TCGA-STAD dataset were used to cluster samples via non-negative matrix factorisation (NMF). Differentially expressed genes (DEGs) between subtypes were identified using the limma package. KEGG pathway and GO functional enrichment analyses were conducted using the WebGestaltR package (v0.4.2). The immune scores of molecular subtypes were evaluated using the R package ESTIMATE, MCPcounter and the ssGSEA function of the GSVA package. Univariate, multivariate and lasso regression analyses of DEGs were performed using the coxph function of the survival package and the glmnet package to construct a RiskScore model. The robustness of the model was validated using internal and external datasets, and a nomogram was constructed based on the model.ResultsBased on 97 tumour invasion-related genes, 353 GC samples from TCGA were categorised into two subtypes, thereby indicating the presence of inter-subtype differences in prognosis. A total of 569 DEGs were identified between the two subtypes; of which, four genes were selected to construct the risk model. This four-gene signature was robust and exhibited stable predictive performance in different platform datasets (GSE26942 and GSE66229), indicating that the established model performed better than other existing models.ConclusionA prognostic stratification system based on a four-gene signature was developed with a desirable area under the curve in the training and independent validation sets. Therefore, the use of this system as a molecular diagnostic test is recommended to assess the prognostic risk of patients with GC.https://www.frontiersin.org/articles/10.3389/fonc.2022.848163/fullgastric cancerinvasionprognosisTCGAimmune |
| spellingShingle | Haonan Guo Hui Tang Yang Zhao Qianwen Zhao Xianliang Hou Lei Ren Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features Frontiers in Oncology gastric cancer invasion prognosis TCGA immune |
| title | Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features |
| title_full | Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features |
| title_fullStr | Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features |
| title_full_unstemmed | Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features |
| title_short | Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features |
| title_sort | molecular typing of gastric cancer based on invasion related genes and prognosis related features |
| topic | gastric cancer invasion prognosis TCGA immune |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.848163/full |
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