Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer
BackgroundAccumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood.MethodsThis research attempted to identify the expression patterns of PANopt...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1222072/full |
| _version_ | 1797741289823797248 |
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| author | Xin Qing Xin Qing Junyi Jiang Chunlei Yuan Kunke Xie Ke Wang |
| author_facet | Xin Qing Xin Qing Junyi Jiang Chunlei Yuan Kunke Xie Ke Wang |
| author_sort | Xin Qing |
| collection | DOAJ |
| description | BackgroundAccumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood.MethodsThis research attempted to identify the expression patterns of PANoptosis regulators and the immune landscape in GC by integrating the GSE54129 and GSE65801 datasets. We analyzed GC specimens and established molecular clusters associated with PANoptosis-related genes (PRGs) and corresponding immune characteristics. The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. Additionally, this study discussed the relationship between infiltrating immune cells and variables in the selected model.ResultsThis study identified dysregulated PRGs and differential immune activities between GC and normal samples, and further identified two PANoptosis-related molecular clusters in GC. These clusters demonstrated remarkable immunological heterogeneity, with Cluster1 exhibiting abundant immune infiltration. The Support Vector Machine signature was found to have the best discriminative ability, and a 5-gene-based SVM signature was established. This model showed excellent performance in the external validation cohorts, and the nomogram, calibration curve, and DCA indicated its reliability in predicting GC patterns. Further analysis confirmed that the 5 selected variables were remarkably related to infiltrating immune cells and immune-related pathways.ConclusionTaken together, this work demonstrates that the PANoptosis pattern has the potential as a stratification tool for patient risk assessment and a reflection of the immune microenvironment in GC. |
| first_indexed | 2024-03-12T14:24:40Z |
| format | Article |
| id | doaj.art-c5fb06cced374c9684f32374edcabdd6 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-03-12T14:24:40Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-c5fb06cced374c9684f32374edcabdd62023-08-18T11:24:51ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-08-011410.3389/fendo.2023.12220721222072Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancerXin Qing0Xin Qing1Junyi Jiang2Chunlei Yuan3Kunke Xie4Ke Wang5Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, ChinaWest China Hospital, Sichuan University, Chengdu, ChinaClinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, ChinaClinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, ChinaClinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, ChinaClinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, ChinaBackgroundAccumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood.MethodsThis research attempted to identify the expression patterns of PANoptosis regulators and the immune landscape in GC by integrating the GSE54129 and GSE65801 datasets. We analyzed GC specimens and established molecular clusters associated with PANoptosis-related genes (PRGs) and corresponding immune characteristics. The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. Additionally, this study discussed the relationship between infiltrating immune cells and variables in the selected model.ResultsThis study identified dysregulated PRGs and differential immune activities between GC and normal samples, and further identified two PANoptosis-related molecular clusters in GC. These clusters demonstrated remarkable immunological heterogeneity, with Cluster1 exhibiting abundant immune infiltration. The Support Vector Machine signature was found to have the best discriminative ability, and a 5-gene-based SVM signature was established. This model showed excellent performance in the external validation cohorts, and the nomogram, calibration curve, and DCA indicated its reliability in predicting GC patterns. Further analysis confirmed that the 5 selected variables were remarkably related to infiltrating immune cells and immune-related pathways.ConclusionTaken together, this work demonstrates that the PANoptosis pattern has the potential as a stratification tool for patient risk assessment and a reflection of the immune microenvironment in GC.https://www.frontiersin.org/articles/10.3389/fendo.2023.1222072/fullPANoptosisgastric cancermolecular patternsimmune infiltrationmachine learning |
| spellingShingle | Xin Qing Xin Qing Junyi Jiang Chunlei Yuan Kunke Xie Ke Wang Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer Frontiers in Endocrinology PANoptosis gastric cancer molecular patterns immune infiltration machine learning |
| title | Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer |
| title_full | Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer |
| title_fullStr | Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer |
| title_full_unstemmed | Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer |
| title_short | Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer |
| title_sort | expression patterns and immunological characterization of panoptosis related genes in gastric cancer |
| topic | PANoptosis gastric cancer molecular patterns immune infiltration machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1222072/full |
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