Continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancers
Abstract Background Tumor mutation burden (TMB) has been considered a biomarker for utilization of immune checkpoint inhibitors(ICIs), but whole exome sequencing(WES) and cancer gene panel(CGP) based on next generation sequencing for TMB detection are costly. Here, we use transcriptome data of TCGA...
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BMC
2024-12-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02794-8 |
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| author | Beibei Hu Guohui Yin Jialin Zhu Yi Bai Xuren Sun |
| author_facet | Beibei Hu Guohui Yin Jialin Zhu Yi Bai Xuren Sun |
| author_sort | Beibei Hu |
| collection | DOAJ |
| description | Abstract Background Tumor mutation burden (TMB) has been considered a biomarker for utilization of immune checkpoint inhibitors(ICIs), but whole exome sequencing(WES) and cancer gene panel(CGP) based on next generation sequencing for TMB detection are costly. Here, we use transcriptome data of TCGA to construct a model for TMB prediction in gastrointestinal tumors. Methods Transcriptome data, somatic mutation data and clinical data of four gastrointestinal tumors from TCGA, including esophageal cancer (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ). Using R, we performed visual analysis of somatic mutation data, differentially expressed genes (DEGs) function enrichment analysis, gene set enrichment analysis (GSEA), and estimated TMB value in clinic. Finally, a deep neural network (DNN) model was constructed for TMB prediction. Results Visualization of somatic mutation data summarized the classification of mutation, frequency of each mutation type, and top-mutated genes. GSEA showed the enrichment of CD4+/CD8+ T cells in the high TMB group and the activation of tumor suppressing pathways. Single-sample GSEA (ssGSEA) manifested that the high-TMB group had higher level of multiple immune cells infiltration. In addition, distribution of TMB was related to clinical parameters. Like age, M stage, N stage, AJCC stage, and overall survival(OS). After model optimization using genetic algorithm, in the training set, validation set, and testing set, the Pearson relevance coefficient r between predicted values and actual values reaches 0.98, 0.82, and 0.92, respectively; the coefficient of determination R2 is 0.95, 0.82, and 0.7, respectively. Conclusion TMB correlates with clinicopathological parameters in gastrointestinal carcinoma, and patients with high TMB have higher levels of immune infiltration. In addition, the DNN model based on 31 genes predicts TMB of gastrointestinal tumors in a high accuracy. |
| format | Article |
| id | doaj-art-c04e348e8a7b49b1a4774404bc9173e0 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-c04e348e8a7b49b1a4774404bc9173e02024-12-22T12:30:05ZengBMCBMC Medical Informatics and Decision Making1472-69472024-12-0124111410.1186/s12911-024-02794-8Continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancersBeibei Hu0Guohui Yin1Jialin Zhu2Yi Bai3Xuren Sun4Department of Gastroenterology, First Affiliated Hospital of China Medical UniversityKey Laboratory of Traffic Safety On Track (Central South University), Ministry of Education, School of Traffic and Transportation Engineering, Central South UniversityDepartment of Gastroenterology, First Affiliated Hospital of China Medical UniversityDepartment of Gastroenterology, First Affiliated Hospital of China Medical UniversityDepartment of Gastroenterology, First Affiliated Hospital of China Medical UniversityAbstract Background Tumor mutation burden (TMB) has been considered a biomarker for utilization of immune checkpoint inhibitors(ICIs), but whole exome sequencing(WES) and cancer gene panel(CGP) based on next generation sequencing for TMB detection are costly. Here, we use transcriptome data of TCGA to construct a model for TMB prediction in gastrointestinal tumors. Methods Transcriptome data, somatic mutation data and clinical data of four gastrointestinal tumors from TCGA, including esophageal cancer (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ). Using R, we performed visual analysis of somatic mutation data, differentially expressed genes (DEGs) function enrichment analysis, gene set enrichment analysis (GSEA), and estimated TMB value in clinic. Finally, a deep neural network (DNN) model was constructed for TMB prediction. Results Visualization of somatic mutation data summarized the classification of mutation, frequency of each mutation type, and top-mutated genes. GSEA showed the enrichment of CD4+/CD8+ T cells in the high TMB group and the activation of tumor suppressing pathways. Single-sample GSEA (ssGSEA) manifested that the high-TMB group had higher level of multiple immune cells infiltration. In addition, distribution of TMB was related to clinical parameters. Like age, M stage, N stage, AJCC stage, and overall survival(OS). After model optimization using genetic algorithm, in the training set, validation set, and testing set, the Pearson relevance coefficient r between predicted values and actual values reaches 0.98, 0.82, and 0.92, respectively; the coefficient of determination R2 is 0.95, 0.82, and 0.7, respectively. Conclusion TMB correlates with clinicopathological parameters in gastrointestinal carcinoma, and patients with high TMB have higher levels of immune infiltration. In addition, the DNN model based on 31 genes predicts TMB of gastrointestinal tumors in a high accuracy.https://doi.org/10.1186/s12911-024-02794-8Gastrointestinal cancersTumor mutation burdenDeep neural networkTranscriptomePrediction model |
| spellingShingle | Beibei Hu Guohui Yin Jialin Zhu Yi Bai Xuren Sun Continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancers BMC Medical Informatics and Decision Making Gastrointestinal cancers Tumor mutation burden Deep neural network Transcriptome Prediction model |
| title | Continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancers |
| title_full | Continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancers |
| title_fullStr | Continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancers |
| title_full_unstemmed | Continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancers |
| title_short | Continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancers |
| title_sort | continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancers |
| topic | Gastrointestinal cancers Tumor mutation burden Deep neural network Transcriptome Prediction model |
| url | https://doi.org/10.1186/s12911-024-02794-8 |
| work_keys_str_mv | AT beibeihu continuouspredictionfortumormutationburdenbasedontranscriptionaldataingastrointestinalcancers AT guohuiyin continuouspredictionfortumormutationburdenbasedontranscriptionaldataingastrointestinalcancers AT jialinzhu continuouspredictionfortumormutationburdenbasedontranscriptionaldataingastrointestinalcancers AT yibai continuouspredictionfortumormutationburdenbasedontranscriptionaldataingastrointestinalcancers AT xurensun continuouspredictionfortumormutationburdenbasedontranscriptionaldataingastrointestinalcancers |