The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy.

In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.

M7G-related molecular subtypes can predict the prognosis and correlate with immunotherapy and chemotherapy responses in bladder cancer patients.

BACKGROUND: N7-methylguanosine (m7G) is closely associated with tumor prognosis and immune response in many cancer types. The correlation between m7G and bladder cancer (BC) needs further study. We aimed to orchestrate molecular subtypes and identify key genes for BC from the perspective of m7G. METHODS: RNA-seq and clinical data of BC patients were extracted from TCGA and GSE13507 datasets. The patients were subtyped by "ConsensusClusterPlus" and "limma." The clusters were validated by the Kaplan‒Meier curves, univariable and multivariate Cox regression models, the concordance index, and calibration curves. The immunotherapy response was evaluated by immune checkpoints, immune infiltration, TIDE score, and IMvigor210 cohort. Genomics of Drug Sensitivity in Cancer was utilized to predict the chemotherapy response between the clusters. RESULTS: The m7G-related cluster was ultimately established by EIF4G1, NUDT11, NUDT10, and CCNB1. The independent prognostic value of the m7G-related cluster was validated by the TCGA and GSE13507 datasets. The cluster was involved in immune-associated pathways, such as neutrophil degranulation, antigen processing cross-presentation, and signaling by interleukins pathways. Meanwhile, cluster 2 was positively correlated with many immune checkpoints, such as CD274, CTLA4, HAVCR2, LAG3, PDCD1, and PDCD1LG2. The cluster 2 was significantly correlated with a higher TIDE score than the cluster 1. Furthermore, in the IMvigor210 cohort, patients in the cluster 1 had a higher response rate than those in the cluster 2. Patients in the cluster 2 were sensitive to many chemotherapies. CONCLUSIONS: We successfully determined molecular subtypes and identified key genes for BC from the perspective of m7G, thereby providing a roadmap for the evolution of immunotherapy and precision medicine.

An autophagy-related gene prognostic index predicting biochemical recurrence, metastasis, and drug resistance for prostate cancer.

Given the dual role of autophagy presenting in tumorigenesis and inhibition, we established an autophagy-related gene prognostic index (ARGPI) with validation to well predict the biochemical recurrence (BCR), metastasis, as well as chemoresistance for patients with prostate cancer (PCa) who underwent radical radiotherapy or prostatectomy. Then, Lasso and COX regression was used to develop the ARGPI. We performed the whole analyses through R packages (version 3.6.3). Secreted phosphoprotein 1 (SPP1), single-minded 2 (SIM2), serine protease inhibitor b5 (SERPINB5), aldehyde dehydrogenase 2 (ALDH2), and acyl-CoA synthetase long-chain 3 (ACSL3) were eventually used to establish the ARGPI score. Patients were divided into two different-risk groups based on the median ARGPI score, high-risk patients with a higher risk of BCR than low-risk patients (hazard ratio [HR]: 5.46, 95% confidence interval [CI]: 3.23-9.24). The risk of metastasis of high-risk patients was higher than low-risk patients (HR: 11.31, 95% CI: 4.89-26.12). In The Cancer Genome Atlas (TCGA) dataset, we observed similar prognostic value of ARGPI in terms of BCR-free survival (HR: 1.79, 95% CI: 1.07-2.99) and metastasis-free survival (HR: 1.80, 95% CI: 1.16-2.78). ARGPI score showed a diagnostic accuracy of 0.703 for drug resistance. Analysis of gene set enrichment analysis (GSEA) indicated that patients in the high-risk group were significantly positively related to interleukin (IL)-18 signaling pathway. Moreover, ARGPI score was significantly related to cancer-related fibroblasts (CAFs; r = 0.36), macrophages (r = 0.28), stromal score (r = 0.38), immune score (r = 0.35), estimate score (r = 0.39), as well as tumor purity (r = -0.39; all P < 0.05). Drug analysis showed that PI-103 was the common sensitive drug and cell line analysis indicated that PC3 was the common cell line of PI-103 and the definitive gene. In conclusion, we found that ARGPI could predict BCR, metastasis, and chemoresistance in PCa patients who underwent radical radiotherapy or prostatectomy.

Circadian rhythm in prostate cancer: time to take notice of the clock.

The circadian clock is an evolutionary molecular product that is associated with better adaptation to changes in the external environment. Disruption of the circadian rhythm plays a critical role in tumorigenesis of many kinds of cancers, including prostate cancer (PCa). Integrating circadian rhythm into PCa research not only brings a closer understanding of the mechanisms of PCa but also provides new and effective options for the precise treatment of patients with PCa. This review begins with patterns of the circadian clock, highlights the role of the disruption of circadian rhythms in PCa at the epidemiological and molecular levels, and discusses possible new approaches to PCa therapy that target the circadian clock.

Identification of senescence-related molecular subtypes and key genes for prostate cancer.

We identified distinct senescence-related molecular subtypes and critical genes among prostate cancer (PCa) patients undergoing radical prostatectomy (RP) or radical radiotherapy (RT). We conducted all analyses using R software and its suitable packages. Twelve genes, namely, secreted frizzled-related protein 4 (SFRP4), DNA topoisomerase II alpha (TOP2A), pleiotrophin (PTN), family with sequence similarity 107 member A (FAM107A), C-X-C motif chemokine ligand 14 (CXCL14), prostate androgen-regulated mucin-like protein 1 (PARM1), leucine zipper protein 2 (LUZP2), cluster of differentiation 38 (CD38), cartilage oligomeric matrix protein (COMP), vestigial-like family member 3 (VGLL3), apolipoprotein E (APOE), and aldehyde dehydrogenase 2 family member (ALDH2), were eventually used to subtype PCa patients from The Cancer Genome Atlas (TCGA) database and GSE116918, and the molecular subtypes showed good correlations with clinical features. In terms of the tumor immune environment (TME) analysis, compared with cluster 1, cancer-associated fibroblasts (CAFs) scored significantly higher, while endothelial cells scored lower in cluster 2 in TCGA database. There was a statistically significant correlation between both CAFs and endothelial cells with biochemical recurrence (BCR)-free survival for PCa patients undergoing RP. For the GSE116918 database, cluster 2 had significantly lower levels of CAFs and tumor purity and higher levels of stromal, immune, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) scores than cluster 1; in addition, patients with high levels of CAFs, stromal scores, immune scores, and ESTIMATE scores and low levels of tumor purity tended to suffer from BCR. Based on the median of differentially expressed checkpoints, high expression of CD96, hepatitis A virus cellular receptor 2 (HAVCR2), and neuropilin 1 (NRP1) in GSE116918 and high expression of CD160 and tumor necrosis factor (ligand) superfamily member 18 (TNFSF18) in TCGA database were associated with a significantly higher risk of BCR than their counterparts. In conclusion, we first constructed distinct molecular subtypes and critical genes for PCa patients undergoing RP or RT from the fresh perspective of senescence.