Integrated analysis of patients with KEAP1/NFE2L2/CUL3 mutations in lung adenocarcinomas

Abstract

Objectives To explore the clinical features, molecular characteristics, and immune landscape of lung adenocarcinoma patients with KEAP1/NFE2L2/CUL3 mutations. Methods The multi‐omics data from the GDC‐TCGA LUAD project of The Cancer Genome Atlas (TCGA) database were downloaded from the Xena browser. The estimate of the immune infiltration was implemented by using the GSVA analysis and CIBERSORT. The status of KEAP1/NFE2L2/CUL3 mutation in 50 LUAD samples of our department was detected by using Sanger sequencing, following the relative expression level of differentially expressed genes (DEGs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) was validated by IHC and real‐time quantitative polymerase chain reaction (RT‐qPCR). Results The Kaplan–Meier and multivariable Cox regression analyses demonstrated that KEAP1/NFE2L2/CUL3 mutations had independent prognostic value for OS and PFS in LUAD patients. The differential analysis detected 207 upregulated genes (like GSR/UGT1A6) and 447 downregulated genes (such as PIGR). GO, KEGG, and GSEA analyses demonstrated that DEGs were enriched in glutamate metabolism and the immune response. The constructed ceRNA network shows the linkage of differential lncRNAs and mRNAs. Three hundred and nine somatic mutations were detected, alterations in immune infiltration DNA methylations and stemness scores were also founded between the two groups. Eight mutated LUAD patients were detected by Sanger DNA sequencing in 50 surgical patients. GSR and UGT1A6 were validated to express higher in the Mut group, whereas the expression of PIGR was restrained. Furthermore, the IHC staining conducted on paraffin‐embedded tissue emphasizes the consistency of our result. Conclusion This research implemented the comprehensive analysis of KEAP1/NFE2L2/CUL3 somatic mutations in the LUAD patients. Compared with the wild type of LUAD patients, the Mut group shows a large difference in clinical features, RNA sequence, DNA methylation, and immune infiltrations, indicating complex mechanism oncogenesis and also reveals potential therapeutic targets.

Identification of Two Subtypes and Prognostic Characteristics of Lung Adenocarcinoma Based on Pentose Phosphate Metabolic Pathway-Related Long Non-coding RNAs

“…The characteristics of LUAD tumor cells are determined by the stromal and immune cells recruited to and activated in the tumor microenvironment (TME). Additionally, the TME in which tumor cells proliferate, develop, and metastasize, is also infiltrated by immune-related molecules and immune cells (36)(37)(38). Thus, we examined the differences in the TME between different pentose phosphate-related lncRNAs-based subtypes.…”

A Novel Prognostic Signature Revealed the Interaction of Immune Cells in Tumor Microenvironment Based on Single-Cell RNA Sequencing for Lung Adenocarcinoma

“…high-throughput data. We used the R package glmnet to perform the LASSO Cox regression analysis as previously described [14], including a built-in crossvalidation function to adjust the L1 regularization variable lambda for candidate feature selection. The R caret package (https://cran.r-project .org/package=caret) was applied to build the classification of the TCGA-LUAD cohort and assess machine learning classifiers for the classification task.…”

“…Tumor mutational burden (TMB) was calculated as the number of somatic base substitutions or indels per megabase (Mb) of the coding region target territory of the test (currently, 1.11 Mb). The stemness data was from previous researchers' articles [14,16,17].…”

Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies

“…Therefore, a multi-omics signature-based classification scheme for LUAD has been proposed, which could potentially reveal the heterogeneity of LUAD. Recently, a multicomponent classification and integration model of LUAD by integrative histology was reported ( 7 ), revealing crosstalk features between different molecules, but only KEAP1/NFE2L2/CUL3 alterations in LUAD compromised the normal function of the antioxidant signaling pathway, which hardly explains the heterogeneity of LUAD. A computational machine learning prediction model (PReceur) developed based on genomic molecular features, compared with the TNM system, can better predict LUAD recurrence risk and guide clinical treatment ( 8 ).…”