Zechen Yan

As a predominant form of cancer affecting male populations, prostate cancer (PCa) demonstrates notably high incidence rates globally. The significant heterogeneity in tumor microenvironment (TME) composition (including epithelial and diverse cell populations) hinders clear interpretation of gene and biomarker roles in disease advancement and immune response modulation. Through combined analysis of bulk and single-cell RNA sequencing data, this investigation evaluates prostate cancer-related genes’ clinical relevance and prognostic potential. We applied approaches like FindAllMarkers, Dseq2 R package, ssGSEA, and WGCNA with both single-cell and bulk transcriptome scales to uncover genes associated with prognosis. Furthermore, we developed a machine learning approach integrating 14 algorithms and 162 algorithmic combinations to support the formation of consensus immune and prognostic-related signatures (IPRS). The IPRS underwent systematic validation in both training and test cohorts, and a multivariate nomogram was constructed to demonstrate its potential utility in prognosis quantification. Through comprehensive multi-omics analyses, which included genomic, single-cell transcriptomic, and bulk transcriptomic data, we sought to achieve a thorough understanding of prognostic characteristics. Furthermore, we evaluated the clinical applicability of the IPRS in the context of immunotherapy and personalized drug selection. Additionally, we examined and confirmed both the gene expression associated with IPRS and the expression level and function of B-cell adhesion molecule (BCAM) within prostate cancer cells and tissue. We discovered 91 genes associated with prognosis in the TME, with 15 of these genes connected to biochemical recurrence. The consensus IPRS constructed based on a machine learning computational framework demonstrates potential value in prognosis prediction and clinical relevance. Multivariate analysis further supports the possibility of IPRS serving as an independent prognostic marker for prostate cancer disease progression. Significant differences in biological functions, immune infiltration, and genomic mutations were also observed among different risk groups. Significantly, the submap method revealed enhanced immunotherapy responsiveness in high-risk patients while highlighting potential pharmacological targets for certain risk subgroups. We selected a collection of genes relevant to PCa prognosis and immune characteristics, which may serve as potential biomarkers with certain clinical translational value.

Background Previous observational studies have indicated potential associations between certain dietary factors and the incidence of kidney cancer. However, the causal relationship between these elements remains unknown. This study is aimed to investigate the causal relationship between dietary factors and the incidence of kidney cancer through Mendelian randomization (MR) analysis. Methods The study utilized genome-wide association study (GWAS) summary data from the GWAS Catalog and the IEU Open GWAS project databases. The exposure factors included consumption of dessert (n = 20,622), fruit (n = 421,155), alcohol (n = 462,346), and salt (n = 323,995). The outcomes were based on GWAS data for kidney cancer (cases = 1,103, controls = 455,245). Three MR methods, including Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median, were used in the study, with IVW serving as the primary method. Sensitivity analyses such as the MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis were performed to detect heterogeneity and pleiotropy. Results The IVW method demonstrated a significant causal relationship between frequent alcohol consumption (OR = 1.61, 95% CI: 1.02–2.54, P = 0.040) and kidney cancer. In contrast, no causal associations were found with the consumption of alcohol (OR = 1.12, 95% CI: 0.91–1.37, P = 0.280), dessert (OR = 1.23, 95% CI: 0.71–2.12, P = 0.465), fruit (OR = 1.33, 95% CI: 0.59-3.00, P = 0.494) or salt (OR = 0.01, 95% CI: 0.00-1.49, P = 0.073). The sensitivity analyses did not reveal any evidence of pleiotropy. Conclusion This MR study suggests that frequent alcohol consumption can increase the risk of kidney cancer, whereas no significant causal relationships were detected between the consumption of dessert, fruit or salt and the risk of kidney cancer.

Some studies have reported that the gut microbiota can influence adrenal-related hormone levels. However, the causal effects of the gut microbiota on adrenal function remain unknown. Therefore, we employed a two-sample Mendelian randomization (MR) study to systematically investigate the impact of gut microbiota on the function of different regions of the adrenal gland. The summary statistics for gut microbiota and adrenal-related hormones used in the two-sample MR analysis were derived from publicly available genome-wide association studies (GWAS). In the MR analysis, inverse variance weighting (IVW) was used as the primary method, with MR-Egger, weighted median, and cML-MA serving as supplementary methods for causal inference. Sensitivity analyses such as the MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis were used to assess pleiotropy and heterogeneity. We identified 27 causal relationships between 23 gut microbiota and adrenal function using the IVW method. Among these, Sellimonas enhanced the function of the adrenal cortex reticularis zone (beta = 0.008, 95% CI: 0.002–0.013, P = 0.0057). The cML-MA method supported our estimate (beta = 0.009, 95% CI: 0.004–0.013, P = 2 × 10^− 4). Parasutterella, Sutterella, and Anaerofilum affect the functioning of different regions of the adrenal gland. Notably, pleiotropy was not observed. Our findings revealed that the gut microbiota is causally associated with adrenal function. This enhances our understanding of the gut-microbiota-brain axis and provides assistance in the early diagnosis and treatment of adrenal-related diseases in clinical practice.

Pheochromocytoma/paraganglioma (PGPG) is a rare neuroendocrine tumor. Amino acid metabolism is crucial for energy production, redox balance, and metabolic pathways in tumor cell proliferation. This study aimed to build a risk model using amino acid metabolism-related genes, enhancing PGPG diagnosis and treatment decisions. We analyzed RNA-sequencing data from the PCPG cohort in the GEO dataset as our training set and validated our findings using the TCGA dataset and an additional clinical cohort. WGCNA and LASSO were utilized to identify hub genes and develop risk prediction models. The single-sample gene set enrichment analysis, MCPCOUNTER, and ESTIMATE algorithm calculated the relationship between amino acid metabolism and immune cell infiltration in PCPG. The TIDE algorithm predicted the immunotherapy efficacy for PCPG patients. The analysis identified 292 genes with differential expression, which are involved in amino acid metabolism and immune pathways. Six genes (DDC, SYT11, GCLM, PSMB7, TYRO3, AGMAT) were identified as crucial for the risk prediction model. Patients with a high-risk profile demonstrated reduced immune infiltration but potentially higher benefits from immunotherapy. Notably, DDC and SYT11 showed strong diagnostic and prognostic potential. Validation through quantitative Real-Time Polymerase Chain Reaction and immunohistochemistry confirmed their differential expression, underscoring their significance in PCPG diagnosis and in predicting immunotherapy response. This study’s integration of amino acid metabolism-related genes into a risk prediction model offers critical clinical insights for PCPG risk stratification, potential immunotherapy responses, drug development, and treatment planning, marking a significant step forward in the management of this complex condition.