Integrated single-cell and transcriptome sequencing analyses determines a chromatin regulator-based signature for evaluating prognosis in lung adenocarcinoma

Abstract

Background: Accumulating evidence has highlighted the significance of chromatin regulator (CR) in pathogenesis and progression of cancer. However, the prognostic role of CRs in LUAD remains obscure. We aim to detect the prognostic value of CRs in LUAD and create favorable signature for assessing prognosis and clinical value of LUAD patients. Methods: The mRNA sequencing data and clinical information were obtained from TCGA and GEO databases. Gene consensus clustering analysis was utilized to determine the molecular subtype of LUAD. Cox regression methods were employed to set up the CRs-based signature (CRBS) for evaluating survival rate in LUAD. Biological function and signaling pathways were identified by KEGG and GSEA analyses. In addition, we calculated the infiltration level of immunocyte by CIBERSORT algorithm. The expressions of model hub genes were detected in LUAD cell lines by real-time polymerase chain reaction (PCR). Results: KEGG analysis suggested the CRs were mainly involved in histone modification, nuclear division and DNA modification. Consensus clustering analysis identified a novel CRs-associated subtype which divided the combined LUAD cohort into two clusters (C1 = 217 and C2 = 296). We noticed that a remarkable discrepancy in survival rate among two clusters. Then, a total of 120 differentially expressed CRs were enrolled into stepwise Cox analyses. Four hub CRs (CBX7, HMGA2, NPAS2 and PRC1) were selected to create a risk signature which could accurately forecast patient outcomes and differentiate patient risk. GSEA unearthed that mTORC1 pathway, PI3K/Akt/mTOR and p53 pathway were greatly enriched in CRBS-high cohort. Moreover, the infiltration percentages of macrophage M0, macrophage M2, resting NK cells, memory B cells, dendritic cells and mast cells were statistically significantly different in the two groups. PCR assay confirmed the differential expression of four model biomarkers. Conclusions: Altogether, our project developed a robust risk signature based on CRs and offered novel insights into individualized treatment for LUAD cases.