Identification macrophage signatures in prostate cancer by single-cell sequencing and machine learning.

BACKGROUND: The tumor microenvironment (TME) encompasses a variety of cells that influence immune responses and tumor growth, with tumor-associated macrophages (TAM) being a crucial component of the TME. TAM can guide prostate cancer in different directions in response to various external stimuli. METHODS: First, we downloaded prostate cancer single-cell sequencing data and second-generation sequencing data from multiple public databases. From these data, we identified characteristic genes associated with TAM clusters. We then employed machine learning techniques to select the most accurate TAM gene set and developed a TAM-related risk label for prostate cancer. We analyzed the tumor-relatedness of the TAM-related risk label and different risk groups within the population. Finally, we validated the accuracy of the prognostic label using single-cell sequencing data, qPCR, and WB assays, among other methods. RESULTS: In this study, the TAM_2 cell cluster has been identified as promoting the progression of prostate cancer, possibly representing M2 macrophages. The 9 TAM feature genes selected through ten machine learning methods and demonstrated their effectiveness in predicting the progression of prostate cancer patients. Additionally, we have linked these TAM feature genes to clinical pathological characteristics, allowing us to construct a nomogram. This nomogram provides clinical practitioners with a quantitative tool for assessing the prognosis of prostate cancer patients. CONCLUSION: This study has analyzed the potential relationship between TAM and PCa and established a TAM-related prognostic model. It holds promise as a valuable tool for the management and treatment of PCa patients.

Personalized radiomics signature to screen for KIT-11 mutation genotypes among patients with gastrointestinal stromal tumors: a retrospective multicenter study.

OBJECTIVES: Gastrointestinal stromal tumors (GISTs) carrying different KIT exon 11 (KIT-11) mutations exhibit varying prognoses and responses to Imatinib. Herein, we aimed to determine whether computed tomography (CT) radiomics can accurately stratify KIT-11 mutation genotypes to benefit Imatinib therapy and GISTs monitoring. METHODS: Overall, 1143 GISTs from 3 independent centers were separated into a training cohort (TC) or validation cohort (VC). In addition, the KIT-11 mutation genotype was classified into 4 categories: no KIT-11 mutation (K11-NM), point mutations or duplications (K11-PM/D), KIT-11 557/558 deletions (K11-557/558D), and KIT-11 deletion without codons 557/558 involvement (K11-D). Subsequently, radiomic signatures (RS) were generated based on the arterial phase of contrast CT, which were then developed as KIT-11 mutation predictors using 1408 quantitative image features and LASSO regression analysis, with further evaluation of its predictive capability. RESULTS: The TC AUCs for K11-NM, K11-PM/D, K11-557/558D, and K11-D ranged from 0.848 (95% CI 0.812-0.884), 0.759 (95% CI 0.722-0.797), 0.956 (95% CI 0.938-0.974), and 0.876 (95% CI 0.844-0.908), whereas the VC AUCs ranged from 0.723 (95% CI 0.660-0.786), 0.688 (95% CI 0.643-0.732), 0.870 (95% CI 0.824-0.918), and 0.830 (95% CI 0.780-0.878). Macro-weighted AUCs for the KIT-11 mutant genotype ranged from 0.838 (95% CI 0.820-0.855) in the TC to 0.758 (95% CI 0.758-0.784) in VC. TC had an overall accuracy of 0.694 (95%CI 0.660-0.729) for RS-based predictions of the KIT-11 mutant genotype, whereas VC had an accuracy of 0.637 (95%CI 0.595-0.679). CONCLUSIONS: CT radiomics signature exhibited good predictive performance in estimating the KIT-11 mutation genotype, especially in prediction of K11-557/558D genotype. RS-based classification of K11-NM, K11-557/558D, and K11-D patients may be an indication for choice of Imatinib therapy.

Development and External Validation of a Nomogram Including Body Composition Parameters for Predicting Early Recurrence of Hepatocellular Carcinoma After Hepatectomy.

RATIONALE AND OBJECTIVES: Body composition, including adipose and muscle tissues, evaluated by computer tomography is correlated with the prognosis of hepatocellular carcinoma (HCC). However, its relationship with early recurrence (ER) remains unclear. This study aimed at establishing and validating a nomogram based on body composition and clinicopathological indices to predict ER of HCC. MATERIALS AND METHODS: One hundred ninety-five patients from institution A formed the training cohort and internal validation cohort, and 50 patients from institution B formed the external validation cohort. Independent predictors of ER were identified using LASSO and Cox regression analyses. The performance of nomogram was evaluated using the calibration curve, concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA). RESULTS: After data screening, the nomogram was constructed using eight independent predictors of ER, including the tumor size, alpha fetoprotein, body mass index, Edmondson Steiner grade, visceral adipose tissue radiodensity, intermuscular adipose tissue index, intramuscular adipose tissue content, and skeletal muscle area. The calibration curve exhibited excellent concordances, with C-indices of 0.808 (95%CI: 0.771-0.860), 0.802 (95%CI: 0.747-0.942), and 0.804 (95%CI: 0.701-0.861) in training, internal validation, and external validation cohorts, respectively. In addition, compared to conventional staging systems and pure clinical model, the nomogram exhibited a higher AUC and wider range of threshold probabilities in DCA, which indicated better discriminative ability and greater clinical benefit. Finally, patients with nomogram scores of <183.07, 183.07-243.09, and >243.09 were considered to have low, moderate, and high risks of ER, respectively. CONCLUSION: The nomogram exhibits excellent ER predictive ability for patients with HCC who underwent hepatectomy.

nc-RNA-mediated high expression of CDK6 correlates with poor prognosis and immune infiltration in pancreatic cancer.

BACKGROUND: Emerging evidence manifests that cyclin-dependent kinase 6 (CDK6) plays an essential part in the initiation and progression of several types of human cancer, and its descending expression is correlated with an adverse prognosis. However, the precise role of CDK6 in Pancreatic cancer (PC) remains obscure. AIMS: To identify the potential ceRNA regulatory axis of CDK6 in PC and explore its relationship with immune cells and immune checkpoints. MATERIALS & METHODS: Using The Cancer Genome Atlas TCGA and GTEx data analyze the expression and survival of CDK6 in patients in pan-cancer, and cellular experiments were performed to verify the effect of CDK6 on cell function. Using GEPIA and STARBASE databases to analyze prognosis, expression and survival, and identify non coding RNA (ncRNA) that mediates CDK6 overexpression. The TIMER 2.0 database was used for immune correlation analysis. RESULTS: We revealed CDK6 might be an oncogene in PC, and the HOXA11-AS /NR2F1-AS1- miR-454-3p axis was identified as the possible upstream ncRNA-associated pathway of CDK6 in PC. In addition, CDK6 show significant association with three immune checkpoints (PD-L1, PD-L2, and HAVCR2), the infiltration level of immune cells, and immunity biomarkers. DISCUSSION: We discussed some applications of CDK6 in breast cancer, melanoma, and hemorrhagic malignancies. The role of miR-15a-5p, HOXA11-AS and NR2F1-AS1 in tumor development was also discussed based on existing studies. The potential mechanism of CDK6 affecting immune cells in pancreatic cancer was discussed. CONCLUSIONS: Overall, these results established that nc-RNA-mediated high expression of CDK6 is associated with patient outcomes and immune invasion in pancreatic cancer.