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

SCG2 Mediates HNSCC Progression With CCL2/TGFβ1high M2 Macrophage Infiltration

OBJECTIVES

This study aims to unravel the mechanisms underlying M2 macrophage polarization in head and neck squamous cell carcinoma (HNSCC), and identify potential therapeutic targets.

MATERIALS AND METHODS

We conducted an integrated bioinformatic analysis using HNSCC bulk transcriptomes from TCGA and GEO databases to pinpoint critical factors influencing M2 macrophage polarization and tumor prognosis. The significance of these genes was validated in function analysis, single-cell transcriptome datasets, and in vitro experiments. Their mechanisms in modulating M2 macrophage polarization were further explored by gene knockdown, cell coculture, and other assays for quantification.

RESULTS

We identified a novel prognostic signature of five genes associated with M2 macrophage infiltration, in which SCG2 emerged as a pivotal factor in M2 macrophage polarization in HNSCC. High expression of SCG2 in tumor patients correlated with poorer prognoses, and knocking down SCG2 reduced the proliferation and migration of HNSCC cells, disrupting M2 macrophage polarization. Furthermore, interference of SCG2 resulted in a significant decrease in the secretion of pro-tumor cytokines such as CCL2 and TGFβ1.

CONCLUSIONS

Our findings provide deeper insights into the pathogenesis of HNSCC and offer promising therapeutic targets for HNSCC, especially SCG2, to inhibit M2 macrophage polarization and modulate cytokine secretion.

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

The mechanisms underlying lipid metabolic disorders in Parkinson's diseases (PD) remain unclear. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify PD-related modular genes and differentially expressed genes (DEGs). Lipid metabolism-related genes (LMRGs) were extracted from Molecular Signatures Database. Candidate genes were assessed with overlapping modular genes, DEGs, and LMRGs for the purpose of building protein-protein interaction (PPI) networks. Then, biomarkers were generated by machine learning and Backpropagation Neural Network development according to candidate genes. Biomarker-based enrichment and network modulation analyses were executed to investigate related signaling pathways. Following dimensionality reduction clustering and annotation, scRNA-seq was submitted to cellular interactions and trajectory analysis to analyze regulatory mechanisms of critical cells. Finally, qRT-PCR was conducted to confirm the expression of biomarkers in PD patients. Four biomarkers (MSMO1, ELOVL6, AACS, and CERS2) were obtained and highly predictive after analysis mentioned above. Then, OPC, Oli, and Neu cells were the primary expression sites for biomarkers according to scRNA-seq studies. Finally, we confirmed mRNA of MSMO1, ELOVL6 and AACS were downregulated in PD patients comparing with control, while CERS2 was upregulated. In conclusion, MSMO1, ELOVL6, AACS, and CERS2 related to LMRGs could be new biomarkers for diagnosing and treating PD.

[Dynamic trajectory and cell communication of different cell clusters in malignant progression of glioblastoma]

OBJECTIVE

To delve deeply into the dynamic trajectories of cell subpopulations and the communication network among immune cell subgroups during the malignant progression of glioblastoma (GBM), and to endeavor to unearth key risk biomarkers in the GBM malignancy progression, so as to provide a more profound understanding for the treatment and prognosis of this disease by integrating transcriptomic data and clinical information of the GBM patients.

METHODS

Utilizing single-cell sequencing data analysis, we constructed a cell subgroup atlas during the malignant progression of GBM. The Monocle2 tool was employed to build dynamic progression trajectories of the tumor cell subgroups in GBM. Through gene enrichment analysis, we explored the biological processes enriched in genes that significantly changed with the malignancy progression of GBM tumor cell subpopulations. CellChat was used to identify the communication network between the different immune cell subgroups. Survival analysis helped in identifying risk molecular markers that impacted the patient prognosis during the malignant progression of GBM. This method ological approach offered a comprehensive and detailed examination of the cellular and molecular dynamics within GBM, providing a robust framework for understanding the disease' s progression and potential therapeutic targets.

RESULTS

The analysis of single-cell sequencing data identified 6 different cell types, including lymphocytes, pericytes, oligodendrocytes, macrophages, glioma cells, and microglia. The 27 151 cells in the single-cell dataset included 3 881 cells from the patients with low-grade glioma (LGG), 10 166 cells from the patients with newly diagnosed GBM, and 13 104 cells from the patients with recurrent glioma (rGBM). The pseudo-time analysis of the glioma cell subgroups indicated significant cellular heterogeneity during malignant progression. The cell interaction analysis of immune cell subgroups revealed the communication network among the different immune subgroups in GBM malignancy, identifying 22 biologically significant ligand-receptor pairs across 12 key biological pathways. Survival analysis had identified 8 genes related to the prognosis of the GBM patients, among which SERPINE1, COL6A1, SPP1, LTF, C1S, AEBP1, and SAA1L were high-risk genes in the GBM patients, and ABCC8 was low-risk genes in the GBM patients. These findings not only provided new theoretical bases for the treatment of GBM, but also offered fresh insights for the prognosis assessment and treatment decision-making for the GBM patients.

CONCLUSION

This research comprehensively and profoundly reveals the dynamic changes in glioma cell subpopulations and the communication patterns among the immune cell subgroups during the malignant progression of GBM. These findings are of significant importance for understanding the complex biological processes of GBM, providing crucial new insights for precision medicine and treatment decisions in GBM. Through these studies, we hope to provide more effective treatment options and more accurate prognostic assessments for the patients with GBM.

CILP is a potential pan-cancer marker: combined silico study and in vitro analyses

CILP (Cartilage intermediate layer protein), an ECM (extracellular matrix) glycoprotein, is found to be associated with intervertebral disc degeneration, chronic heart failure, obese and cardiac fibrosis. However, there are few reports on the role of CILP in tumors. Thus, in this study, we mainly explored the function of CILP in the occurrence and development of tumors and whether it could be a potential pan-cancer marker. Pan-cancer data in this study were obtained from UCSC Xena. Single-cell data were obtained from GSE152938. ROC (Receiver operating characteristic) curves were used to evaluate the accuracy of CILP in predicting the occurrence of different tumor types. The Kaplan-Meier plots were used to assess the relationship between CILP expression and survival prognosis in different tumor types by COX regression analysis. Pseudotime analysis and cell communication analysis were used to further explore the function of CILP at Single cell level. The human RCC (renal cell carcinoma) cell lines ACHN and 786-O were used for further experimental verification. Bulk RNA-seq showed differences in CILP expression in several tumors. ROC curves showed that 14 tumors have AUC > 0.7. Kaplan-Meier plots indicated that CILP is a risk factor for patients in 3 kinds of tumors. ScRNA-seq (Single cell RNA sequencing) suggested that CILP might influence tumors through fibroblasts and cell-cell communication. Finally, we verified the function of CILP at the cellular level by using RCC cell lines ACHN and 786-O and found that knockdown of CILP could significantly inhibit migration and invasion. This finding supports that CILP could be a risk factor as well as a pan-cancer predictor for patients.

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

Acute myeloid leukemia (AML) is a complex mixed entity composed of malignant tumor cells, immune cells and stromal cells, with intra-tumor and inter-tumor heterogeneity. Single-cell RNA sequencing enables a comprehensive study of the highly complex tumor microenvironment, which is conducive to exploring the evolutionary trajectory of tumor cells. Herein, we carried out comprehensive analyses of aggrephagy-related cell clusters based on single-cell sequencing for patients with acute myeloid leukemia. A total of 11 specific cell types (T, NK, CMP, Myeloid, GMP, MEP, Promono, Plasma, HSC, B, and Erythroid cells) using t-SNE dimension reduction analysis. Several aggrephagy-related genes were highly expressed in the 11 specific cell types. Using Monocle analysis and NMF clustering analysis, six aggrephagy-related CD8+ T clusters, six aggrephagy-related NK clusters, and six aggrephagy-related Mac clusters were identified. We also evaluated the ligand-receptor links and Cell-cell communication using CellChat package and CellChatDB database. Furthermore, the transcription factors (TFs) of aggrephagy-mediated cell clusters for AML were assessed through pySCENIC package. Prognostic analysis of the aggrephagy-related cell clusters based on R package revealed the differences in prognosis of aggrephagy-mediated cell clusters. Immunotherapy of the aggrephagy-related cell clusters was investigated using TIDE algorithm and public immunotherapy cohorts. Our study revealed the significance of aggrephagy-related patterns in tumor microenvironment, prognosis, and immunotherapy for AML.