Background: Cancer stem cells (CSCs) are a subset of cells within tumors that possess the unique ability to self-renew and give rise to diverse tumor cells. These cells are crucial in driving tumor metastasis, recurrence, and resistance to treatment. The objective of this study was to pinpoint the essential regulatory genes associated with CSCs in prostate adenocarcinoma (PRAD) and assess their potential significance in the diagnosis, prognosis, and immunotherapy of patients with PRAD.Method: The study utilized single-cell analysis techniques to identify stem cell-related genes and evaluate their significance in relation to patient prognosis and immunotherapy in PRAD through cluster analysis. By utilizing diverse datasets and employing various machine learning methods for clustering, diagnostic models for PRAD were developed and validated. The random forest algorithm pinpointed HSPE1 as the most crucial prognostic gene among the stem cell-related genes. Furthermore, the study delved into the association between HSPE1 and immune infiltration, and employed molecular docking to investigate the relationship between HSPE1 and its associated compounds. Immunofluorescence staining analysis of 60 PRAD tissue samples confirmed the expression of HSPE1 and its correlation with patient prognosis in PRAD.Result: This study identified 15 crucial stem cell-related genes through single-cell analysis, highlighting their importance in diagnosing, prognosticating, and potentially treating PRAD patients. HSPE1 was specifically linked to PRAD prognosis and response to immunotherapy, with experimental data supporting its upregulation in PRAD and association with poorer prognosis.Conclusion: Overall, our findings underscore the significant role of stem cell-related genes in PRAD and unveil HSPE1 as a novel target related to stem cell.

Background: Benign prostatic hyperplasia (BPH) is a common issue among older men. Diagnosis of BPH currently relies on imaging tests and assessment of urinary flow rate due to the absence of definitive diagnostic markers. Developing more accurate markers is crucial to improve BPH diagnosis.Method: The BPH dataset utilized in this study was sourced from the Gene Expression Omnibus (GEO). Initially, differential expression and functional analyses were conducted, followed by the application of multiple machine learning techniques to identify key diagnostic markers. Subsequent investigations have focused on elucidating the functions and mechanisms associated with these markers. The ssGSEA method was employed to evaluate immune cell scores in BPH samples, facilitating the exploration of the relationship between key diagnostic markers and immune cells. Additionally, molecular docking was performed to assess the binding affinity of these key markers to therapeutic drugs for BPH. Tissue samples from BPH patients were collected for experimental validation of the expression differences of the aforementioned genes.Result: A total of 185 differential genes were identified, comprising 67 up-regulated and 118 down-regulated genes. These genes are implicated in pathways that regulate extracellular matrix tissue composition and cellular responses to transforming growth factor beta stimulation, as well as critical signaling pathways such as AMPK and mTOR. Through the application of various machine learning techniques, DACH1, CACNA1D, STARD13, and RUNDC3B were identified as key diagnostic markers. The ssGSEA algorithm further corroborated the association of these diagnostic genes with diverse immune cells. Moreover, molecular docking analysis revealed strong binding affinities of these markers to tamsulosin and finasteride, suggesting their potential as drug targets. Finally, experimental validation confirmed the expression differences of DACH1, CACNA1D, STARD13, and RUNDC3B in BPH tissues.Conclusion: This study introduces novel immune-related diagnostic markers for BPH and highlights their promise as new drug targets, providing a valuable approach for predictive diagnosis and targeted therapy of BPH.

Background: Monocytes play a critical role in tumor initiation and progression, with their impact on prostate adenocarcinoma (PRAD) not yet fully understood. This study aimed to identify key monocyte-related genes and elucidate their mechanisms in PRAD.Method: Utilizing the TCGA-PRAD dataset, immune cell infiltration levels were assessed using CIBERSORT, and their correlation with patient prognosis was analyzed. The WGCNA method pinpointed 14 crucial monocyte-related genes. A diagnostic model focused on monocytes was developed using a combination of machine learning algorithms, while a prognostic model was created using the LASSO algorithm, both of which were validated. Random forest and gradient boosting machine singled out CCNA2 as the most significant gene related to prognosis in monocytes, with its function further investigated through gene enrichment analysis. Mendelian randomization analysis of the association of HLA-DR high-expressing monocytes with PRAD. Molecular docking was employed to assess the binding affinity of CCNA2 with targeted drugs for PRAD, and experimental validation confirmed the expression and prognostic value of CCNA2 in PRAD.Result: Based on the identification of 14 monocyte-related genes by WGCNA, we developed a diagnostic model for PRAD using a combination of multiple machine learning algorithms. Additionally, we constructed a prognostic model using the LASSO algorithm, both of which demonstrated excellent predictive capabilities. Analysis with random forest and gradient boosting machine algorithms further supported the potential prognostic value of CCNA2 in PRAD. Gene enrichment analysis revealed the association of CCNA2 with the regulation of cell cycle and cellular senescence in PRAD. Mendelian randomization analysis confirmed that monocytes expressing high levels of HLA-DR may promote PRAD. Molecular docking results suggested a strong affinity of CCNA2 for drugs targeting PRAD. Furthermore, immunohistochemistry experiments validated the upregulation of CCNA2 expression in PRAD and its correlation with patient prognosis.Conclusion: Our findings offer new insights into monocyte heterogeneity and its role in PRAD. Furthermore, CCNA2 holds potential as a novel targeted drug for PRAD.

Background Colonic adenocarcinoma (COAD) is the most common pathological type of colon cancer. Tumor microenvironment (TME) plays an important role in the occurrence and development of COAD. There are currently no specific studies indicating the mechanism of action of TME in COPD patients. Methods The percentage of tumor-infiltrating immune cells (TIC) in 512 COAD cases from The Cancer Genome Atlas (TCGA) database was calculated using CIBERSORT and ESTIMATE. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to determine the function of distant metastasis (M)-stage-related modules. Pathway enrichment analysis, protein–protein interaction (PPI) network, Cox regression analysis, and Kaplan–Meier survival analysis were performed on DEGs to select the most critical genes. The correlation between SIGLEC1 expression in COAD and TME status and between immune checkpoints and SIGLEC1 was examined using gene set enrichment analysis (GSEA) and Pearson correlation coefficients. Results A WGCNA screen was performed to obtain 12,342 DEGs and 209 key genes associated with M stage between tumor and normal samples. GO and KEGG analysis revealed that the DEGs primarily engaged in pathways such as Th1 and Th2 cell differentiation and cell adhesion molecules. SIGELEC1 gene was identified by univariate Cox regression, PPI network construction, and survival analysis. GSEA showed that the genes in the high-expression SIGLEC1 group were mainly enriched in immune-related activities. In the low-expression SIGLEC1 group, the genes were enriched in MYC targets. CIBERSORT analysis of the proportion of TICs showed that SIGLEC1 was positively correlated with macrophages (M0, M2), T-cell CD8 and immune checkpoint-related genes, suggesting that SIGLEC1 may be responsible for maintaining the immune dominance of TME. Immunohistochemical and prognostic analysis showed that the group with higher SIGLEC1 expression had more severe lesions and a worse prognosis than the group with lower SIGLEC1 expression. Conclusions SIGLEC1 gene is a distant metastasis-related gene that affects the survival prognosis of COAD patients and provides additional insight into the treatment of COAD.

Endothelial cells are integral components of the tumor microenvironment and play a multifaceted role in tumor immunotherapy. Targeting endothelial cells and related signaling pathways can improve the effectiveness of immunotherapy by normalizing tumor blood vessels and promoting immune cell infiltration. However, to date, there have been no comprehensive studies analyzing the role of endothelial cells in the diagnosis and treatment of prostate adenocarcinoma (PRAD). By integrating clinical and transcriptomic data from TCGA-PRAD, we initially identified key endothelial cell-related genes in PRAD samples through single-cell analysis. Subsequently, cluster analysis was employed to classify PRAD samples based on the expression of these endothelial cell-related genes, allowing us to explore their correlation with patient prognosis and immunotherapy outcomes. A diagnostic model was then constructed and validated using a combination of 108 machine learning algorithms. The XGBoost and Random Forest algorithms highlighted the significant role of COL1A1, and we further analyzed the expression and correlation of COL1A1, AR, and EGFR through multiplex immunofluorescence staining. In vitro experimental analysis of the impact of COL1A1 on the progression of PRAD. Single-cell analysis identified 12 differential prognostic genes associated with endothelial cells. Cluster analysis confirmed a strong correlation between endothelial cell-related genes and both prostate cancer prognosis and immunotherapy responses. Diagnostic models developed using various machine learning techniques demonstrated the significant predictive capability of these 12 genes in the diagnosis of prostate cancer. Furthermore, based on patients’ prognostic information, multiple machine learning analyses highlighted the critical role of COL1A1. Immunofluorescence analysis results confirmed that COL1A1 is highly expressed in prostate cancer and is positively correlated with both AR and EGFR. In vitro experiments confirm that reducing COL1A1 expression levels can inhibit PRAD progression. This study provides a comprehensive analysis of the role of endothelial cell-related genes in the diagnosis, prognosis, and immunotherapy of prostate cancer. The findings, supported by various machine learning algorithms and experimental results, highlight COL1A1 as a significant target for the diagnosis and immunotherapy of PRAD.