Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating cancer cells, offer substantial insights into malignant tumour diagnosis, treatment and prognosis. This study aims to provide a model based on 15 types of Programmed Cell Death-Related Genes (PCDRGs) for evaluating immune microenvironment and prognosis in ccRCC patients. ccRCC patients from the TCGA and arrayexpress cohorts were grouped based on PCDRGs. A combination model using Lasso and SuperPC was constructed to identify prognostic gene features. The arrayexpress cohort validated the model, confirming its robustness. Immune microenvironment analysis, facilitated by PCDRGs, employed various methods, including CIBERSORT. Drug sensitivity analysis guided clinical treatment decisions. Single-cell data enabled Programmed Cell Death-Related scoring, subsequent pseudo-temporal and cell-cell communication analyses. A PCDRGs signature was established using TCGA-KIRC data. External validation in the arrayexpress cohort underscored the model's superiority over traditional clinical features. Furthermore, our single-cell analysis unveiled the roles of PCDRG-based single-cell subgroups in ccRCC, both in pseudo-temporal progression and intercellular communication. Finally, we performed CCK-8 assay and other experiments to investigate csf2. In conclusion, these findings reveal that csf2 inhibit the growth, infiltration and movement of cells associated with renal clear cell carcinoma. This study introduces a PCDRGs prognostic model benefiting ccRCC patients while shedding light on the pivotal role of programmed cell death genes in shaping the immune microenvironment of ccRCC patients.

Biocontrol activity and potential mechanism of volatile organic compounds from Aspergillus niger strain La2 against pear Valsa canker.

BACKGROUND: Valsa canker caused by Valsa pyri is one of the most destructive diseases of pear, leading to severe yield and economic losses. Volatile organic compounds (VOCs) from endophytes have important roles in the regulation of plant disease. In this study, we investigated the biocontrol activity of the endophytic fungus Aspergillus niger strain La2 and its antagonistic VOCs against pear Valsa canker. RESULTS: Strain La2 exhibited an obvious inhibitory effect against V. pyri. A colonization assay suggested that strain La2 could complete its life cycle on pear twigs. The symptoms of pear Valsa canker were weakened on detached pear twigs after treatment with strain La2. In addition, VOCs from strain La2 also significantly suppressed mycelial growth in V. pyri. Based on the results of headspace solid-phase microextraction/gas chromatography-mass spectrometry analysis, six possible VOCs produced by strain La2 were detected, of which 2,4-di-tert-butylphenol and 4-methyl-1-pentanol were the main antagonistic VOCs in terms of their effect on pear Valsa canker in vitro and in vivo. Further results showed that 4-methyl-1-pentanol could destroy the V. pyri hyphal structure and cell membrane integrity. Importantly, the activities of pear defense-related enzymes (polyphenol oxidase, phenylalanine ammonia lyase and superoxide dismutase) were enhanced after 4-methyl-1-pentanol treatment in pear twigs, suggesting that 4-methyl-1-pentanol might induce a plant disease resistance response. CONCLUSION: Aspergillus niger strain La2 and its VOCs 2,4-di-tert-butylphenol and 4-methyl-1-pentanol have potential as novel biocontrol agents of pear Valsa canker. © 2024 Society of Chemical Industry.

A Five Glutamine-Associated Signature Predicts Prognosis of Prostate Cancer and Links Glutamine Metabolism with Tumor Microenvironment.

Glutamine has been recognized as an important amino acid that provide a variety of intermediate products to fuel biosynthesis. Glutamine metabolism participates in the progression of the tumor via various mechanisms. However, glutamine-metabolism-associated signatures and its significance in prostate cancer are still unclear. In this current study, we identified five genes associated with glutamine metabolism by univariate and Lasso regression analysis and constructed a model to predict the biochemical recurrence free survival (BCRFS) of PCa. Further validation of the prognostic risk model demonstrated a good efficacy in predicting the BCRFS in PCa patients. Interestingly, based on the CIBERSORTx, ssGSEA and ESTIMATE algorithms predictions, we noticed a distinct immune cell infiltration and immune pathway pattern in the prediction of the two risk groups stratified by the risk model. Drug sensitivity prediction revealed that patients in the high-risk group were more suitable for chemotherapy. Last but not least, glutamine deprivation significantly inhibited cell growth in GLUL or ASNS knock down prostate cancer cell lines. Therefore, we proposed a novel prognostic model by using glutamine metabolism genes for PCa patients and identified potential mechanism of PCa progression through glutamine-related tumor microenvironment remodeling.

A Novel Four Mitochondrial Respiration-Related Signature for Predicting Biochemical Recurrence of Prostate Cancer.

The biochemical recurrence (BCR) of patients with prostate cancer (PCa) after radical prostatectomy is high, and mitochondrial respiration is reported to be associated with the metabolism in PCa development. This study aimed to establish a mitochondrial respiratory gene-based risk model to predict the BCR of PCa. RNA sequencing data of PCa were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and mitochondrial respiratory-related genes (MRGs) were sourced via GeneCards. The differentially expressed mitochondrial respiratory and BCR-related genes (DE-MR-BCRGs) were acquired through overlapping BCR-related differentially expressed genes (BCR-DEGs) and differentially expressed MRGs (DE-MRGs) between PCa samples and controls. Further, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were performed to construct a DE-MRGs-based risk model. Then, a nomogram was established by analyzing the independent prognostic factor of five clinical features and risk scores. Moreover, Gene Set Enrichment Analysis (GSEA), tumor microenvironment, and drug susceptibility analyses were employed between high- and low-risk groups of PCa patients with BCR. Finally, qRT-PCR was utilized to validate the expression of prognostic genes. We identified 11 DE-MR-BCRGs by overlapping 132 DE-MRGs and 13 BCR-DEGs and constructed a risk model consisting of 4 genes (APOE, DNAH8, EME2, and KIF5A). Furthermore, we established an accurate nomogram, including a risk score and a Gleason score, for the BCR prediction of PCa patients. The GSEA result suggested the risk model was related to the PPAR signaling pathway, the cholesterol catabolic process, the organic hydroxy compound biosynthetic process, the small molecule catabolic process, and the steroid catabolic process. Simultaneously, we found six immune cell types relevant to the risk model: resting memory CD4+ T cells, monocytes, resting mast cells, activated memory CD4+ T cells, regulatory T cells (Tregs), and macrophages M2. Moreover, the risk model could affect the IC50 of 12 cancer drugs, including Lapatinib, Bicalutamide, and Embelin. Finally, qRT-PCR showed that APOE, EME2, and DNAH8 were highly expressed in PCa, while KIF5A was downregulated in PCa. Collectively, a mitochondrial respiratory gene-based nomogram including four genes and one clinical feature was established for BCR prediction in patients with PCa, which could provide novel strategies for further studies.

A novel prognostic model based on three integrin subunit genes-related signature for bladder cancer.

Background: Presently, a comprehensive analysis of integrin subunit genes (ITGs) in bladder cancer (BLCA) is absent. This study endeavored to thoroughly analyze the utility of ITGs in BLCA through computer algorithm-based bioinformatics. Methods: BLCA-related materials were sourced from reputable databases, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). R software-based bioinformatics analyses included limma-differential expression analysis, survival-Cox analysis, glmnet-Least absolute shrinkage and selection operator (LASSO), clusterProfiler-functional annotation, and gsva-estimate-immune landscape analysis. The expression difference of key genes was verified by quantitative real-time polymerase chain reaction (qRT-PCR). Results: Among the 11 ITGs that were abnormally expressed in BLCA, ITGA7, ITGA5, and ITGB6 were categorized as the optimal variables for structuring the risk model. The high-risk subcategories were typified by brief survival, abysmal prognosis, prominent immune and stromal markers, and depressed tumor purity. The risk model was also an isolated indicator of the impact of clinical outcomes in BLCA patients. Moreover, the risk model, specifically the high-risk subcategory with inferior prognosis, became heavily interlinked with the immune-inflammatory response and smooth muscle contraction and relaxation. Conclusion: This study determined three ITGs with prognostic values (ITGA7, ITGA5, and ITGB6), composed a novel (ITG-associated) prognostic gene signature, and preliminarily probed the latent molecular mechanisms of the model.

Peri- and postoperative outcomes of laparoscopic adrenalectomy in nonobese versus obese patients: a systematic review and meta-analysis.

Introduction: Obesity is generally thought to increase the difficulty and complications of surgery. Laparoscopic adrenalectomy has become the standard operation for adrenal tumors at present. Aim: To assess whether laparoscopic adrenalectomy (LA) can be used for obese patients with adrenal tumor. Material and methods: We systematically searched PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), and Science databases and Cochrane Library, and the search time is up to January 2022. We used STATA 16.0 and RevMan 5.4 software for data processing and statistical analysis. Results: Eight studies were included in the meta-analysis. The meta-analysis results showed that compared with the nonobese group, the obese group had a significantly longer operation time (OT) (weighted mean difference (WMD): -10.02, 95% confidence interval (CI): -19.16 to 0.87, p = 0.03). It also had higher estimated blood loss (WMD: -13.15, 95% CI: -35.92 to 9.63, p = 0.26) and conversion rate (odds ratio (OR): 0.70, 95% CI: 1.31 to 1.60, p = 0.40), longer length of hospital stay (LOS) (WMD: -0.04, 95% CI: -0.47 to 0.39, p = 0.86), and a higher number of complications (odds ratio (OR) = 0.71, 95% CI: 0.49 to 1.02, p = 0.06), but these differences were not statistically significant. Additionally, in subgroup analysis longer OT (p = 0.0001) and LOS (p = 0.007) were associated with retroperitoneal laparoscopic adrenalectomy for obesity. Conclusions: Our meta-analysis suggests that LA is feasible and effective in patients with obesity.