Novel biomarkers predict prognosis and drug-induced neuroendocrine differentiation in patients with prostate cancer

Abstract

BackgroundA huge focus is being placed on the development of novel signatures in the form of new combinatorial regimens to distinguish the neuroendocrine (NE) characteristics from castration resistant prostate cancer (CRPC) timely and accurately, as well as predict the disease-free survival (DFS) and progression-free survival (PFS) of prostate cancer (PCa) patients.MethodsSingle cell data of 4 normal samples, 3 CRPC samples and 3 CRPC-NE samples were obtained from GEO database, and CellChatDB was used for potential intercellular communication, Secondly, using the “limma” package (v3.52.0), we obtained the differential expressed genes between CRPC and CRPC-NE both in single-cell RNA seq and bulk RNA seq samples, and discovered 12 differential genes characterized by CRPC-NE. Then, on the one hand, the diagnosis model of CRPC-NE is developed by random forest algorithm and artificial neural network (ANN) through Cbioportal database; On the other hand, using the data in Cbioportal and GEO database, the DFS and PFS prognostic model of PCa was established and verified through univariate Cox analysis, least absolute shrinkage and selection operator (Lasso) regression and multivariate Cox regression in R software. Finally, somatic mutation and immune infiltration were also discussed.ResultsOur research shows that there exists specific intercellular communication in classified clusters. Secondly, a CRPC-NE diagnostic model of six genes (HMGN2, MLLT11, SOX4, PCSK1N, RGS16 and PTMA) has been established and verified, the area under the ROC curve (AUC) is as high as 0.952 (95% CI: 0.882−0.994). The mutation landscape shows that these six genes are rarely mutated in the CRPC and NEPC samples. In addition, NE-DFS signature (STMN1 and PCSK1N) and NE-PFS signature (STMN1, UBE2S and HMGN2) are good predictors of DFS and PFS in PCa patients and better than other clinical features. Lastly, the infiltration levels of plasma cells, T cells CD4 naive, Eosinophils and Monocytes were significantly different between the CRPC and NEPC groups.ConclusionsThis study revealed the heterogeneity between CRPC and CRPC-NE from different perspectives, and developed a reliable diagnostic model of CRPC-NE and robust prognostic models for PCa.

Identification of cell differentiation trajectory-related gene signature to reveal the prognostic significance and immune landscape in prostate cancer based on multiomics analysis

“…Principal clusters were then identified by visualizing the high-dimensional data through the t-distributed random neighbor embedding (tSNE) approach. The data processing procedures closely followed the method described previously [ 24 ].…”

“…Data analysis was performed using R (version 4.2.1), a statistical software application with extensively documented statistical methods in the literature [ 24 , 37 ]. The comparison of data between the two groups was executed using the t -test, with a threshold for statistical significance set at P < 0.05.…”

Aggrephagy-related patterns in tumor microenvironment, prognosis, and immunotherapy for acute myeloid leukemia: a comprehensive single-cell RNA sequencing analysis

“…The signaling inputs and outputs among the cell types and aggrephagy-mediated cell clusters were assessed by applying CellChat package ( 44 ) and CellChatDB database ( 45 ). The netVisual_circle function was utilized for evaluating the strength of cell–cell communication networks among cell subsets ( 44 , 45 ). In addition, the ligand-receptor interactions among the specific cell subsets were estimated via the netVisual_bubble function ( 44 , 45 ).…”

“…The netVisual_circle function was utilized for evaluating the strength of cell–cell communication networks among cell subsets ( 44 , 45 ). In addition, the ligand-receptor interactions among the specific cell subsets were estimated via the netVisual_bubble function ( 44 , 45 ).…”

Identifying MSMO1, ELOVL6, AACS, and CERS2 related to lipid metabolism as biomarkers of Parkinson's disease

“…Employing SingleR package (v 2.0.0) 22 and marker genes from the literature 23 , clusters were annotated and biomarkers' expression in clusters was evaluated. Cell expression data were utilized as input to investigate interactions between different cells using CellChat (v 1.6.1) 24 on the basis of the CellChatDB database 25 . Following that, the most widely distributed cells were subjected to trajectory analysis via Monocle software (v 2.26.0) 26 to determine their differentiation paths.…”