Construction of a gene model related to the prognosis of patients with gastric cancer receiving immunotherapy and exploration of COX7A1 gene function
Abstract Background GC is a highly heterogeneous tumor with different responses to immunotherapy, and the positive response depends on the unique interaction between the tumor and the tumor microenvironment (TME). However, the currently available methods for prognostic prediction are not satisfactory. Therefore, this study aims to construct a novel model that integrates relevant gene sets to predict the clinical efficacy of immunotherapy and the prognosis of GC patients based on machine learning. Methods Seven GC datasets were collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database and literature sources. Based on the immunotherapy cohort, we first obtained a list of immunotherapy related genes through differential expression analysis. Then, Cox regression analysis was applied to divide these genes with prognostic significancy into protective and risky types. Then, the Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to score the two categories of gene sets separately, and the scores differences between the two gene sets were used as the basis for constructing the prognostic model. Subsequently, Weighted Correlation Network Analysis (WGCNA) and Cytoscape were applied to further screen the gene sets of the constructed model, and finally COX7A1 was selected for the exploration and prediction of the relationship between the clinical efficacy of immunotherapy for GC. The correlation between COX7A1 and immune cell infiltration, drug sensitivity scoring, and immunohistochemical staining were performed to initially understand the potential role of COX7A1 in the development and progression of GC. Finally, the differential expression of COX7A1 was verified in those GC patients receiving immunotherapy. Results First, 47 protective genes and 408 risky genes were obtained, and the ssGSEA algorithm was applied for model construction, showing good prognostic discrimination ability. In addition, the patients with high model scores showed higher TMB and MSI levels, and lower tumor heterogeneity scores. Then, it is found that the COX7A1 expressions in GC tissues were significantly lower than those in their corresponding paracancerous tissues. Meanwhile, the patients with high COX7A1 expression showed higher probability of cancer invasion, worse clinical efficacy of immunotherapy, worse overall survival (OS) and worse disease-free survival (DFS). Conclusions The ssGSEA score we constructed can serve as a biomarker for GC patients and provide important guidance for individualized treatment. In addition, the COX7A1 gene can accurately distinguish the prognosis of GC patients and predict the clinical efficacy of immunotherapy for GC patients..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
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| Enthalten in: |
European Journal of Medical Research - 29(2024), 1, Seite 19 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Si-yu Wang [VerfasserIn] |
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| Links: |
doi.org [kostenfrei] |
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| Themen: |
Biomarker |
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| doi: |
10.1186/s40001-024-01783-x |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
DOAJ095744800 |
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| 520 | |a Abstract Background GC is a highly heterogeneous tumor with different responses to immunotherapy, and the positive response depends on the unique interaction between the tumor and the tumor microenvironment (TME). However, the currently available methods for prognostic prediction are not satisfactory. Therefore, this study aims to construct a novel model that integrates relevant gene sets to predict the clinical efficacy of immunotherapy and the prognosis of GC patients based on machine learning. Methods Seven GC datasets were collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database and literature sources. Based on the immunotherapy cohort, we first obtained a list of immunotherapy related genes through differential expression analysis. Then, Cox regression analysis was applied to divide these genes with prognostic significancy into protective and risky types. Then, the Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to score the two categories of gene sets separately, and the scores differences between the two gene sets were used as the basis for constructing the prognostic model. Subsequently, Weighted Correlation Network Analysis (WGCNA) and Cytoscape were applied to further screen the gene sets of the constructed model, and finally COX7A1 was selected for the exploration and prediction of the relationship between the clinical efficacy of immunotherapy for GC. The correlation between COX7A1 and immune cell infiltration, drug sensitivity scoring, and immunohistochemical staining were performed to initially understand the potential role of COX7A1 in the development and progression of GC. Finally, the differential expression of COX7A1 was verified in those GC patients receiving immunotherapy. Results First, 47 protective genes and 408 risky genes were obtained, and the ssGSEA algorithm was applied for model construction, showing good prognostic discrimination ability. In addition, the patients with high model scores showed higher TMB and MSI levels, and lower tumor heterogeneity scores. Then, it is found that the COX7A1 expressions in GC tissues were significantly lower than those in their corresponding paracancerous tissues. Meanwhile, the patients with high COX7A1 expression showed higher probability of cancer invasion, worse clinical efficacy of immunotherapy, worse overall survival (OS) and worse disease-free survival (DFS). Conclusions The ssGSEA score we constructed can serve as a biomarker for GC patients and provide important guidance for individualized treatment. In addition, the COX7A1 gene can accurately distinguish the prognosis of GC patients and predict the clinical efficacy of immunotherapy for GC patients. | ||
| 650 | 4 | |a Gastric cancer (GC) | |
| 650 | 4 | |a Biomarker | |
| 650 | 4 | |a Prognostic model | |
| 650 | 4 | |a Immunotherapy | |
| 650 | 4 | |a COX7A1 | |
| 650 | 4 | |a Gene function | |
| 653 | 0 | |a Medicine | |
| 653 | 0 | |a R | |
| 700 | 0 | |a Yu-xin Wang |e verfasserin |4 aut | |
| 700 | 0 | |a Ao Shen |e verfasserin |4 aut | |
| 700 | 0 | |a Xian-qi Yang |e verfasserin |4 aut | |
| 700 | 0 | |a Cheng-cai Liang |e verfasserin |4 aut | |
| 700 | 0 | |a Run-jie Huang |e verfasserin |4 aut | |
| 700 | 0 | |a Rui Jian |e verfasserin |4 aut | |
| 700 | 0 | |a Nan An |e verfasserin |4 aut | |
| 700 | 0 | |a Yu-long Xiao |e verfasserin |4 aut | |
| 700 | 0 | |a Li-shuai Wang |e verfasserin |4 aut | |
| 700 | 0 | |a Yin Zhao |e verfasserin |4 aut | |
| 700 | 0 | |a Chuan Lin |e verfasserin |4 aut | |
| 700 | 0 | |a Chang-ping Wang |e verfasserin |4 aut | |
| 700 | 0 | |a Zhi-ping Yuan |e verfasserin |4 aut | |
| 700 | 0 | |a Shu-qiang Yuan |e verfasserin |4 aut | |
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