Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma

Abstract

Background Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adenocarcinoma (LUAD) are largely unknown. Methods In this study, 1658 LUAD patients from different cohorts were included. In addition, 724 cancer patients who received immunotherapy were also included. To identify DC marker genes in LUAD, we used single-cell RNAsequencing data for analysis and determined 83 genes as DC marker genes. Following that, integrative machine learning procedure was developed to construct a signature for DC marker genes. Results Using TCGA bulk-RNA sequencing data as the training set, we developed a signature consisting of seven genes and classified patients by their risk status. Another six independent cohorts demonstrated the signature’ s prognostic power, and multivariate analysis demonstrated it was an independent prognostic factor. LUAD patients in the high-risk group displayed more advanced features, discriminatory immune-cell infiltrations and immunosuppressive states. Cell–cell communication analysis indicates that tumor cells with lower risk scores communicate more actively with the tumor microenvironment. Eight independent immunotherapy cohorts revealed that patients with low-risk had better immunotherapy responses. Drug sensitivity analysis indicated that targeted therapy agents exhibited greater sensitivity to low-risk patients, while chemotherapy agents displayed greater sensitivity to high-risk patients. In vitro experiments confirmed that CTSH is a novel protective factor for LUAD. Conclusions An unique signature based on DC marker genes that is highly predictive of LUAD patients’ prognosis and response to immunotherapy. CTSH is a new biomarker for LUAD.

Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response

“…Leveraging ten machine learning algorithms (GBM, RSF, SuperPC, Survival-SVM, Lasso, stepwise Cox, Ridge, Enet, CoxBoost, and plsRcox), we developed an integrative Iron, Copper, and Sulfur-Metabolism Index (ICSMI) via the CoxBoost + GBM combination. After conducting a thorough evaluation of 114 varied permutations, we opted for this selection, which mirrored our previous approach [ 20 ]. The detailed introduce of each algorithm and the specific implementations of various combinations were illustrated in Supplementary Methods.…”

“…com/ IMvig or210 CoreB iolog ies [18]. The single-cell RNA-sequencing dataset GSE127465 was acquired from the TISCH database [19] and processed in accordance with previously outlined procedures [20]. The genes associated with iron and copper metabolism were compiled from previously published research [21][22][23][24].…”

In-depth single-cell and bulk-RNA sequencing developed a NETosis-related gene signature affects non-small-cell lung cancer prognosis and tumor microenvironment: results from over 3,000 patients

“…The GSE127465 dataset includes single-cell RNA-sequencing information for 7 primary LUAD samples. That data was downloaded from TISCH ( 17 ) and processed as described in previous ( 18 ). Gene sets for NETosis were compiled from previously published studies ( Table S1 ) ( 19 – 21 ).…”