ScRNA-seq revealed an immunosuppression state and tumor microenvironment heterogeneity related to lymph node metastasis in prostate cancer

The implications for urological malignancies of non-coding RNAs in the the tumor microenvironment

Urological malignancies are a major global health issue because of their complexity and the wide range of ways they affect patients. There's a growing need for in-depth research into these cancers, especially at the molecular level. Recent studies have highlighted the importance of non-coding RNAs (ncRNAs) – these don't code for proteins but are crucial in controlling genes – and the tumor microenvironment (TME), which is no longer seen as just a background factor but as an active player in cancer progression. Understanding how ncRNAs and the TME interact is key for finding new ways to diagnose and predict outcomes in urological cancers, and for developing new treatments. This article reviews the basic features of ncRNAs and goes into detail about their various roles in the TME, focusing specifically on how different ncRNAs function and act in urological malignancies.

BMSC derived EVs inhibit colorectal Cancer progression by transporting MAGI2-AS3 or something similar

In this study, we investigated the molecular mechanisms underlying the impact of extracellular vesicles (EVs) derived from bone marrow stromal cells (BMSCs) on colorectal cancer (CRC) development. The focus was on the role of MAGI2-AS3, delivered by BMSC-EVs, in regulating USP6NL DNA methylation-mediated MYC protein translation modification to promote CDK2 downregulation. Utilizing bioinformatics analysis, we identified significant enrichment of MAGI2-AS3 related to copper-induced cell death in CRC. In vitro experiments demonstrated the downregulation of MAGI2-AS3 in CRC cells, and BMSC-EVs were found to deliver MAGI2-AS3 to inhibit CRC cell proliferation, migration, and invasion. Further exploration revealed that MAGI2-AS3 suppressed MYC protein translation modification by regulating USP6NL DNA methylation, leading to CDK2 downregulation and prevention of colorectal cancer. Overexpression of MYC reversed the functional effects of BMSC-EVs-MAGI2-AS3. In vivo experiments validated the inhibitory impact of BMSC-EVs-MAGI2-AS3 on CRC tumorigenicity by promoting CDK2 downregulation through USP6NL DNA methylation-mediated MYC protein translation modification. Overall, BMSC-EVs-MAGI2-AS3 may serve as a potential intervention to prevent CRC occurrence by modulating key molecular pathways.

Spatial proteomic profiling elucidates immune determinants of neoadjuvant chemo-immunotherapy in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) presents significant clinical and therapeutic challenges due to its aggressive nature and generally poor prognosis. We initiated a Phase II clinical trial (ChiCTR1900027160) to assess the efficacy of a pioneering neoadjuvant chemo-immunotherapy regimen comprising programmed death-1 (PD-1) blockade (Toripalimab), nanoparticle albumin-bound paclitaxel (nab-paclitaxel), and the oral fluoropyrimidine derivative S-1, in patients with locally advanced ESCC. This study uniquely integrates clinical outcomes with advanced spatial proteomic profiling using Imaging Mass Cytometry (IMC) to elucidate the dynamics within the tumor microenvironment (TME), focusing on the mechanistic interplay of resistance and response. Sixty patients participated, receiving the combination therapy prior to surgical resection. Our findings demonstrated a major pathological response (MPR) in 62% of patients and a pathological complete response (pCR) in 29%. The IMC analysis provided a detailed regional assessment, revealing that the spatial arrangement of immune cells, particularly CD8+ T cells and B cells within tertiary lymphoid structures (TLS), and S100A9+ inflammatory macrophages in fibrotic regions are predictive of therapeutic outcomes. Employing machine learning approaches, such as support vector machine (SVM) and random forest (RF) analysis, we identified critical spatial features linked to drug resistance and developed predictive models for drug response, achieving an area under the curve (AUC) of 97%. These insights underscore the vital role of integrating spatial proteomics into clinical trials to dissect TME dynamics thoroughly, paving the way for personalized and precise cancer treatment strategies in ESCC. This holistic approach not only enhances our understanding of the mechanistic basis behind drug resistance but also sets a robust foundation for optimizing therapeutic interventions in ESCC.

Metastatic hormone-naïve prostate cancer: a distinct biological entity

Metastatic hormone-naïve prostate cancer (mHNPC) is often the initial form of presentation for metastatic prostate cancer and encompasses a heterogeneous patient population with high inter-patient heterogeneity in prognosis and response to therapy. A more precise treatment of mHNPC, guided by evidence-based biomarkers, remains an unmet medical need. In addition, the limited number of representative laboratory models of mHNPC hampers the translation of basic research into clinical applications. We provide a comprehensive overview of the clinical and biological features that characterize mHNPC, highlight molecular data that could explain the unique prognostic characteristics of mHNPC, and identify key open questions.

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

Background

In the context of prostate cancer (PCa), the occurrence of biochemical recurrence (BCR) stands out as a pivotal factor significantly impacting prognosis, potentially leading to metastasis and mortality. However, the early detection of BCR poses a substantial challenge for PCa patients. There is an urgent need to pinpoint hub genes that can serve as predictive indicators for BCR in PCa patients.

Methods

Our primary goal was to identify cell differentiation trajectory-related gene signature in PCa patients by pseudo-time trajectory analysis. We further explored the functional enrichment of overlapped marker genes and probed clinically relevant modules and BCR-related genes using Weighted Gene Co-expression Network Analysis (WGCNA) in PCa patients. Key genes predicting recurrence-free survival were meticulously identified through univariate and multivariate Cox regression analyses. Subsequently, these genes were utilized to construct a prognostic gene signature, the expression, predictive efficacy, putative functions, and immunological landscape of which were thoroughly validated. Additionally, we employed immunohistochemistry (IHC) and a western blotting assay to quantify the expression of PYCR1 in clinical samples.

Results

Our single-cell RNA (scRNA) sequencing analysis unveiled three subgroups characterized by distinct differentiation trajectories, and the marker genes associated with these groups were extracted from PCa patients. These marker genes successfully classified the PCa sample into two molecular subtypes, demonstrating a robust correlation with clinical characteristics and recurrence-free survival. Through WGCNA and Lasso analysis, we identified four hub genes (KLK3, CD38, FASN, and PYCR1) to construct a risk profile of prognostic genes linked to BCR. Notably, the high-risk patient group exhibited elevated levels of B cell naive, Macrophage M0, and Macrophage M2 infiltration, while the low-risk group displayed higher levels of T cells CD4 memory activated and monocyte infiltration. Furthermore, IHC and western blotting assays confirmed the heightened expression of PYCR1 in PCa tissues.

Conclusion

This study leveraged the differentiation trajectory and genetic variability of the microenvironment to uncover crucial prognostic genes associated with BCR in PCa patients. These findings present novel perspectives for tailoring treatment strategies for PCa patients on an individualized basis.

It's all about the base: stromal cells are central orchestrators of metastasis

The tumor microenvironment (TME) is an integral part of tumors and plays a central role in all stages of carcinogenesis and progression. Each organ has a unique and heterogeneous microenvironment, which affects the ability of disseminated cells to grow in the new and sometimes hostile metastatic niche. Resident stromal cells, such as fibroblasts, osteoblasts, and astrocytes, are essential culprits in the modulation of metastatic progression: they transition from being sentinels of tissue integrity to being dysfunctional perpetrators that support metastatic outgrowth. Therefore, better understanding of the complexity of their reciprocal interactions with cancer cells and with other components of the TME is essential to enable the design of novel therapeutic approaches to prevent metastatic relapse.

Machine learning and bioinformatics analysis to identify autophagy-related biomarkers in peripheral blood for rheumatoid arthritis

Background: Although rheumatoid arthritis (RA) is a common autoimmune disease, the precise pathogenesis of the disease remains unclear. Recent research has unraveled the role of autophagy in the development of RA. This research aims to explore autophagy-related diagnostic biomarkers in the peripheral blood of RA patients. Methods: The gene expression profiles of GSE17755 were retrieved from the gene expression ontology (GEO) database. Differentially expressed autophagy-related genes (DE-ARGs) were identified for the subsequent research by inserting autophagy-related genes and differentially expressed genes (DEGs). Three machine learning algorithms, including random forest, support vector machine recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), were employed to identify diagnostic biomarkers. A nomogram model was constructed to assess the diagnostic value of the biomarkers. The CIBERSORT algorithm was performed to investigate the correlation of the diagnostic biomarkers with immune cells and immune factors. Finally, the diagnostic efficacy and differential expression trend of diagnostic biomarkers were validated in multiple cohorts containing different tissues and diseases. Results: In this study, 25 DE-ARGs were identified between RA and healthy individuals. In addition to "macroautophagy" and "autophagy-animal," DE-ARGs were also associated with several types of programmed cell death and immune-related pathways according to GO and KEGG analysis. Three diagnostic biomarkers, EEF2, HSP90AB1 and TNFSF10, were identified by the random forest, SVM-RFE, and LASSO. The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988-0.999). Furthermore, immune infiltration analysis showed a remarkable association between EEF2, HSP90AB1, and TNFSF10 expression with various immune cells and immune factors. The three diagnostic biomarkers also exhibited good diagnostic efficacy and demonstrated the same trend of differential expression in multiple validation cohorts. Conclusion: This study identified autophagy-related diagnostic biomarkers based on three machine learning algorithms, providing promising targets for the diagnosis and treatment of RA.

Advances in single-cell RNA sequencing and its applications in cancer research

Cancers are a group of heterogeneous diseases characterized by the acquisition of functional capabilities during the transition from a normal to a neoplastic state. Powerful experimental and computational tools can be applied to elucidate the mechanisms of occurrence, progression, metastasis, and drug resistance; however, challenges remain. Bulk RNA sequencing techniques only reflect the average gene expression in a sample, making it difficult to understand tumor heterogeneity and the tumor microenvironment. The emergence and development of single-cell RNA sequencing (scRNA-seq) technologies have provided opportunities to understand subtle changes in tumor biology by identifying distinct cell subpopulations, dissecting the tumor microenvironment, and characterizing cellular genomic mutations. Recently, scRNA-seq technology has been increasingly used in cancer studies to explore tumor heterogeneity and the tumor microenvironment, which has increased the understanding of tumorigenesis and evolution. This review summarizes the basic processes and development of scRNA-seq technologies and their increasing applications in cancer research and clinical practice.