基于 m5C 相关免疫基因的肺腺癌机器学习预后模型的建立与验证。
Development and Validation of a Machine Learning Prognostic Model of m5C Related immune Genes in Lung Adenocarcinoma.
机构信息
The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, China.
出版信息
Cancer Control. 2024 Jan-Dec;31:10732748241237414. doi: 10.1177/10732748241237414.
BACKGROUND
The aim of this retrospective research was to develop an immune-related genes significantly associated with m5C methylation methylation (m5C-IRGs)-related signature associated with lung adenocarainoma (LUAD).
METHODS
We introduced transcriptome data to screen out m5C-IRGs in The Cancer Genome Atlas (TCGA)-LUAD dataset. Subsequently, the m5C-IRGs associated with survival were certificated by Kaplan Meier (K-M) analysis. The univariate Cox, least absolute shrinkage and selection operator (LASSO) regression, and xgboost.surv tool were adopted to build a LUAD prognostic signature. We further conducted gene functional enrichment, immune microenvironment and immunotherapy analysis between 2 risk subgroups. Finally, we verified m5C-IRGs-related prognostic gene expression in transcription level.
RESULTS
A total of 76 m5C-IRGs were identified in TCGA-LUAD dataset. Furthermore, 27 m5C-IRGs associated with survival were retained. Then, a m5C-IRGs prognostic signature was build based on the 3 prognostic genes (HLA-DMB, PPIA, and GPI). Independent prognostic analysis suggested that stage and RiskScore could be used as independent prognostic factors. We found that 4104 differentially expressed genes (DEGs) between the 2 risk subgroups were mainly concerned in immune receptor pathways. We found certain distinction in LUAD immune microenvironment between the 2 risk subgroups. Then, immunotherapy analysis and chemotherapeutic drug sensitivity results indicated that the m5C-IRGs-related gene signature might be applied as a therapy predictor. Finally, we found significant higher expression of PPIA and GPI in LUAD group compared to the normal group.
CONCLUSIONS
The prognostic signature comprised of HLA-DMB, PPIA, and GPI based on m5C-IRGs was established, which might provide theoretical basis and reference value for the research of LUAD.
PUBLIC DATASETS ANALYZED IN THE STUDY
TCGA-LUAD dataset was collected from the TCGA (https://portal.gdc.cancer.gov/) database, GSE31210 (validation set) was retrieved from GEO (https://www.ncbi.nlm.nih.gov/geo/) database.
背景
本回顾性研究旨在开发与 m5C 甲基化（m5C-IRGs）相关的基因特征，以关联肺腺癌（LUAD）。
方法
我们引入转录组数据从癌症基因组图谱（TCGA）-LUAD 数据集中筛选 m5C-IRGs。随后，通过 Kaplan-Meier（K-M）分析验证与生存相关的 m5C-IRGs。采用单变量 Cox、最小绝对值收缩和选择算子（LASSO）回归和 xgboost.surv 工具构建 LUAD 预后签名。我们进一步在 2 个风险亚组之间进行基因功能富集、免疫微环境和免疫治疗分析。最后，我们在转录水平验证 m5C-IRGs 相关的预后基因表达。
结果
在 TCGA-LUAD 数据集中鉴定出 76 个 m5C-IRGs，其中 27 个 m5C-IRGs 与生存相关。然后，基于 3 个预后基因（HLA-DMB、PPIA 和 GPI）构建了 m5C-IRGs 预后签名。独立预后分析表明，分期和风险评分可以作为独立的预后因素。我们发现，2 个风险亚组之间有 4104 个差异表达基因（DEGs），主要涉及免疫受体途径。我们发现，2 个风险亚组之间的 LUAD 免疫微环境存在一定差异。然后，免疫治疗分析和化疗药物敏感性结果表明，m5C-IRGs 相关基因特征可能作为治疗预测指标。最后，我们发现 LUAD 组中 PPIA 和 GPI 的表达明显高于正常组。
结论
基于 m5C-IRGs 构建的 HLA-DMB、PPIA 和 GPI 预后签名，为 LUAD 的研究提供了理论依据和参考价值。
本研究中分析的公共数据集
TCGA-LUAD 数据集从 TCGA（https://portal.gdc.cancer.gov/）数据库收集，GSE31210（验证集）从 GEO（https://www.ncbi.nlm.nih.gov/geo/）数据库检索。
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