Construction and experimental validation of an acetylation-related gene signature to evaluate the recurrence and immunotherapeutic response in early-stage lung adenocarcinoma

Abstract

Background Acetylation is a reversible epigenetic process, playing an important role in the initiation and progression of malignant tumors. However, the prognosis value of acetylation-related genes in the early-stage lung adenocarcinoma (LUAD) remains obscure. Materials and methods The acetylation-related genes were collected and clustered based on transcriptome sequencing of the patients with early-stage LUAD from the Cancer Genome Atlas. The genomic divergence analysis, protein–protein interaction network construction, Lasso regression, and univariate Cox regression were used to identify the significant biomarkers for the recurrence of the early-stage LUAD. The multivariate Cox regression was used to establish the predictive model. Gene Expression Omnibus was systemically retrieved and four independent datasets were used for external validation. 23 early-stage LUAD samples were collected from the local hospital to detect the expression difference of the genes in the model. Transfection assays were performed to verify the regulatory ability of the screened gene to the proliferation of LUAD cell lines. The single-cell RNA sequencing of the early-stage LUAD patients and two lung cancer cohorts receiving immunotherapy were utilized to explore the predictive ability of the established model to immunotherapeutic sensitivity. Results The clustering based on acetylation-related genes was significantly associated with the recurrence (P < 0.01) and immune infiltration statuses. Through a series of bioinformatical and machine learning methods, RBBP7 and YEATS2 were ultimately identified. Accordingly, a novel gene signature containing RBBP7 and YEATS2 was developed to evaluate the recurrence-free survival of early-stage LUAD, which was then validated in five independent cohorts (pooled hazard ratio = 1.88, 95% confidence interval = 1.49–2.37) and 23 local clinical samples (P < 0.01). The knock-down of YEATS2 obviously suppressed proliferation of H1975 and HCC-827 cells. Single-cell RNA sequencing analyses indicated that RBBP7 and YEATS2 were both associated with the tumor immune response, and the prognosis signature could predict the immunotherapeutic response in two cohorts receiving immunotherapy (P < 0.05; P < 0.01). Conclusions Totally, an acetylation-related gene signature is constructed, helping to evaluate the recurrence and immunotherapeutic effectiveness of early-stage LUAD patients.

Development and experimental validation of an M2 macrophage and platelet‐associated gene signature to predict prognosis and immunotherapy sensitivity in bladder cancer

“…Recently, the establishment of diagnosis or prognosis models from the particular aspect of biological processes, such as ferroptosis, 45,46 necrosis, 47 lipid metabolism, 48,49 and acetylation, 50 and the tumor immune microenvironment, 51,52 has attracted increasing interest.…”

“…With the rise and development of big‐data analysis and high‐throughput sequencing, an increasing number of predictive models based on molecular expression levels have been proposed, helping to guide personalized treatment and reduce the costs of treatment. Recently, the establishment of diagnosis or prognosis models from the particular aspect of biological processes, such as ferroptosis, 45 , 46 necrosis, 47 lipid metabolism, 48 , 49 and acetylation, 50 and the tumor immune microenvironment, 51 , 52 has attracted increasing interest. Similar to these studies, the established model in our study was based on platelets and M2 macrophages.…”

Interpreting the molecular mechanisms of RBBP4/7 and their roles in human diseases (Review)

“…Research has revealed that the expression levels of RBBP7 in NSCLC are higher than those in normal lung tissues, and this elevated expression is associated with distant metastasis, poor prognosis and tumor immune response in NSCLC, serving as a predictor for the recurrence of early-stage NSCLC (88)(89)(90).…”

The artificial intelligence and machine learning in lung cancer immunotherapy

“…Some scholars collected acetylation-related genes in the transcriptome of early LUAD patients in the TCGA database to try to identify the important biomarkers of early LUAD recurrence through differential analysis and protein–protein interaction network construction. Finally, they concluded that the two gene signatures of RBBP7 and YEATS2 can be used to predict the recurrence-free survival of early LUAD [ 56 ].…”