Exploration of the molecular characteristics of the tumor-immune interaction and the development of an individualized immune prognostic signature for neuroblastoma.

Neuroblastoma (NBL) exists in a complex tumor-immune microenvironment. Immune cell infiltration and tumor-immune molecules play a critical role in tumor development and significantly impact the prognosis of patients. However, the molecular characteristics describing the NBL-immune interaction and their prognostic potential have yet to be investigated systematically. We first employed multiple machine learning algorithms, such as Gene Sets Enrichment Analysis and cell type identification by estimating relative subsets of RNA transcripts, to identify immunophenotypes and immunological characteristics in NBL patient data from public databases and then investigated the prognostic potential and regulatory networks of identified immune-related genes involved in the NBL-immune interaction. The immunity signature combining nine immunity genes was confirmed as more effective for individual risk stratification and survival outcome prediction in NBL patients than common clinical characteristics (area under the curve [AUC] = 0.819, C-index = 0.718, p < .001). A mechanistic exploration revealed the regulatory network of molecules involved in the NBL-immune interaction. These immune molecules were also discovered to possess a significant correlation with plasma cell infiltration, MYCN status, and the level of chemokines and macrophage-related molecules (p < .001). A nomogram was constructed based on the immune signature and clinical characteristics, which showed high potential for prognosis prediction (AUC = 0.856, C-index = 0.755, p < .001). We systematically elucidated the complex regulatory mechanisms and characteristics of the molecules involved in the NBL-immune interaction and their prognostic potential, which may have important implications for further understanding the molecular mechanism of the NBL-immune interaction and identifying high-risk NBL patients to guide clinical treatment.

Comprehensive role of prostate-specific antigen identified with proteomic analysis in prostate cancer.

Current treatments including androgen deprivation fail to prevent prostate cancer (PrCa) from progressing to castration-resistant PrCa (CRPC). Accumulating evidence highlights the relevance of prostate-specific antigen (PSA) in the development and progression of PrCa. The underlying mechanism whereby PSA functions in PrCa, however, has yet been elucidated. We demonstrated that PSA knockdown attenuated tumorigenesis and metastasis of PrCa C4-2 cells in vitro and in vivo, whereas promoted the apoptosis in vitro. To illuminate the comprehensive role of PSA in PrCa, we performed an isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic analysis to explore the proteomic change induced by PSA knockdown. Among 121 differentially expressed proteins, 67 proteins were up-regulated, while 54 proteins down-regulated. Bioinformatics analysis was used to explore the mechanism through which PSA exerts influence on PrCa. Protein-protein interaction analysis showed that PSA may mediate POTEF, EPHA3, RAD51C, HPGD and MCM4 to promote the initiation and progression of PrCa. We confirmed that PSA knockdown induced the up-regulation of MCM4 and RAD51C, while it down-regulated POTEF and EPHA3; meanwhile, MCM4 was higher in PrCa para-cancerous tissue than in cancerous tissue, suggesting that PSA may facilitate the tumorigenesis by mediating MCM4. Our findings suggest that PSA plays a comprehensive role in the development and progression of PrCa.

Tailored machine learning for evaluating the long-term diabetes risk in older individuals: findings from the Irish Longitudinal Study on Ageing (TILDA).

OBJECTIVES: The prevalence of diabetes has increased globally, leading to a significant disease burden and financial cost. Early prediction is crucial to control its prevalence. DESIGN: A prospective cohort study. SETTING: National representative study on Irish. PARTICIPANTS: 8504 individuals aged 50 years or older were included. PRIMARY AND SECONDARY OUTCOME MEASURES: Surveys were conducted to collect over 40 000 variables related to social, financial, health, mental and family status. Feature selection was performed using logistic regression. Different machine/deep learning algorithms were trained, including distributed random forest, extremely randomised trees, a generalised linear model with regularisation, a gradient boosting machine and a deep neural network. These algorithms were integrated into a stacked ensemble to generate the best model. The model was tested using various metrics, such as the area under the curve (AUC), log loss, mean per classification error, mean square error (MSE) and root MSE (RMSE). The SHapley Additive exPlanations (SHAP) method was used to interpret the established model. RESULTS: After 2 years, 105 baseline features were identified as major contributors to diabetes risk, including sex, low-density lipoprotein cholesterol and cirrhosis. The best model achieved high accuracy, robustness and discrimination in predicting diabetes risk, with an AUC of 0.854, log loss of 0.187, mean per classification error of 0.267, RMSE of 0.229 and MSE of 0.052 in the independent test set. The model was also shown to be well calibrated. The SHAP algorithm provided insights into the decision-making process of the model. CONCLUSIONS: These findings could help physicians in the early identification of high-risk patients and implement targeted interventions to reduce diabetes incidence.

AID-induced CXCL12 upregulation enhances castration-resistant prostate cancer cell metastasis by stabilizing ß-catenin expression.

Prostate cancer (PCa) is one of the most common malignant diseases of urinary system and has poor prognosis after progression to castration-resistant prostate cancer (CRPC), and increased cytosine methylation heterogeneity is associated with the more aggressive phenotype of PCa cell line. Activation-induced cytidine deaminase (AID) is a multifunctional enzyme and contributes to antibody diversification. However, the dysregulation of AID participates in the progression of multiple diseases and related with certain oncogenes through demethylation. Nevertheless, the role of AID in PCa remains elusive. We observed a significant upregulation of AID expression in PCa samples, which exhibited a negative correlation with E-cadherin expression. Furthermore, AID expression is remarkably higher in CRPC cells than that in HSPC cells, and AID induced the demethylation of CXCL12, which is required to stabilize the Wnt signaling pathway executor ß-catenin and EMT procedure. Our study suggests that AID drives CRPC metastasis by demethylation and can be a potential therapeutic target for CRPC.

A novel inflammation-associated prognostic signature for clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) are typically situated in a complex inflammatory and immune microenvironment, which has been reported to contribute to the unfavorable prognosis of patients with ccRCC. There would be beneficial clinical implications for elucidating the roles of its molecular characteristics in the inflammatory microenvironment. This is because it would facilitate the development of reliable biomarkers for pre-stratification prior to the designation of individualized treatment strategies. In the present study, RNA-sequencing data from 607 patients were retrospectively analyzed to elucidate the profile of inflammatory molecules. Based on this, an inflammatory prognostic signature (IPS) was developed and further validated using clinical ccRCC samples. Subsequently, the associated mechanisms in terms of the immune microenvironment and molecular pathways were then investigated. This proposed IPS was found to exhibit superior accuracy compared with the criterion of a good prognostic model for the prediction of patient prognosis from ccRCC [area under the receiver operating characteristic curve (AUC)=0.811] in addition to being an independent factor for prognostic risk stratification [hazard ratio: 11.73 (95% CI, 26.98-5.10); log-rank test, P<0.001]. Pathologically, ccRCC cells identified as high-risk according to their IPS presented with a more malignant tumor structure, including voluminous eosinophilic cytoplasm, acinar/lamellar/tubular growth patterns and atypic nuclei. High-risk ccRCC also exhibited higher infiltration levels by four types of immune cells, including T regulatory cells, but lower infiltration levels by mast cells. Pathways associated with immune-inflammation interaction, including the IL-17 pathway, were found to be upregulated in IPS-identified high-risk ccRCC. Furthermore, by combining the IPS with clinical factors, an integrated prognostic index was developed and validated for increasing the accuracy of patient risk-stratification for ccRCC (AUC=0.911). In conclusion, the complex regulatory mechanisms and molecular characteristics involved in ccRCC-inflammation interaction, coupled with their prognostic potential, were systematically elucidated in the present study. This may have important implications in furthering the understanding into the molecular mechanisms underlying this ccRCC-inflammation interaction, which can in turn be exploited for identifying high-risk patients with ccRCC prior to designing their clinical treatment strategy.

The Effect of Individual Musculoskeletal Conditions on Depression: Updated Insights From an Irish Longitudinal Study on Aging.

Few longitudinal studies have systematically investigated whether or how individual musculoskeletal conditions (IMCs) convey risks for negative psychological health outcomes, and approaches to assess such risk in the older population are lacking. In this Irish nationally representative longitudinal prospective study of 6,715 individuals aged 50 and above, machine learning algorithms and various models, including mediation models, were employed to elaborate the underlying mechanisms of IMCs leading to depression and to develop an IMC-induced negative psychological risk (IMCPR) classification approach. Resultantly, arthritis [odds ratio (95% confidence interval): 2.233 (1.700-2.927)], osteoporosis [1.681 (1.133-2.421)], and musculoskeletal chronic pain [MCP, 2.404 (1.838-3.151)] were found to increase the risk of depression after 2 years, while fracture and joint replacement did not. Interestingly, mediation models further demonstrated that arthritis per se did not increase the risk of depression; such risk was augmented only when arthritis-induced restrictions of activities (ARA) existed [proportion of mediation: 316.3% (ARA of usual), 213.3% (ARA of social and leisure), and 251.3% (ARA of sleep)]. The random forest algorithm attested that osteoarthritis, not rheumatoid arthritis, contributed the most to depressive symptoms. Moreover, bone mineral density was negatively associated with depressive symptoms. Systemic pain contributed the most to the increased risk of depression, followed by back, knee, hip, and foot pain (mean Gini-Index: 3.778, 2.442, 1.980, 1.438, and 0.879, respectively). Based on the aforementioned findings, the IMCPR classification approach was developed using an interpretable machine learning model, which stratifies participants into three grades. Among the IMCPR grades, patients with a grade of "severe" had higher odds of depression than those with a "mild" [odds ratio (95% confidence interval): 4.055 (2.907-5.498)] or "moderate" [3.584 (2.101-5.883)] grade. Females with a "severe" grade had higher odds of depression by 334.0% relative to those with a "mild" grade, while males had a relative risk of 258.4%. In conclusion, the present data provide systematic insights into the IMC-induced depression risk and updated the related clinical knowledge. Furthermore, the IMCPR classification approach could be used as an effective tool to evaluate this risk.

Do eye diseases increase the risk of arthritis in the elderly population?

There are very few longitudinal studies which have previously conducted an investigation into whether eye diseases are a risk for arthritis, and how this occurs. The study employed a variety of machine-learning algorithms, including random forest for investigating the risks, and to elucidate these underlying mechanisms by focusing on five aspects containing 389 characterized variables (mental health and wellbeing; physical health; disability, functional impairment and helpers; health behavior; and health measures). The study population included 8,423 individuals. Cataracts, glaucoma, and other eye diseases increase the likelihood of arthritis after two years by 131.8% (odds ratio (OR)=2.318, 95% confidence interval: 1.748 to 3.038), 123.1% (OR=2.231, 1.306 to 3.626), and 91.1% (OR=1.911, 1.501 to 2.415). Random forest corroborated that cataract contributes the most to arthritis risks after two years, followed by other eye diseases and glaucoma (mean Gini-index: 5.20, 2.11, 1.31). It is of note that the potential mechanisms of cataract-induced arthritis risk were elucidated extensively. The control domains of life quality, negative aging self-perceptions, mobility (steadiness, physical limitations, and muscle strength) and memory impairments, and sleep quality mediated the relationship between cataracts and arthritis significantly. Furthermore, different eye diseases affected osteoarthritis, rheumatoid arthritis, and other arthritis to varying degrees. Eye diseases increased the risk of arthritis, whereby cataracts were the most significant. Interventions which target these discovered mechanisms may be the preferred levers for reducing cataract-related arthritis risk.

Activation-Induced Cytidine Deaminase Expression Facilitates the Malignant Phenotype and Epithelial-to-Mesenchymal Transition in Clear Cell Renal Cell Carcinoma.

Although advances have been made in the development of antiangiogenesis targeted therapy and surgery, metastatic clear cell renal cell carcinoma (ccRCC) is still incurable. Activation-induced cytidine deaminase (AID) is mainly expressed in a variety of germ and somatic cells, and induces somatic hypermutation and class-switch recombination, playing a vital role in antibody diversification. We confirmed that AID was expressed at a higher level in ccRCC tissues than in the corresponding nontumor renal tissues. We explored the impact of AID on ccRCC proliferation, invasion, and migration. In 769-p and 786-0 cells, expression of an AID-specific short hairpin RNA significantly reduced AID expression, which markedly inhibited tumor cell invasion, proliferation, and migration. Previous studies showed that AID is associated with Wnt ligand secretion mediator (WLS/GPR177), cyclin-dependent kinase 4 (CDK4), and stromal cell-derived factor-1 (SDF-1/CXCL12) regulation, which was further confirmed in human ccRCC tissues. Therefore, we studied the relationship between AID and these three molecules, and the impact of AID on epithelial-to-mesenchymal transition in ccRCC. WLS/GPR177, SDF-1/CXCL12, and CDK4 were sensitive to 5-azacytidine (a DNA demethylation agent), which reverted the inhibition of carcinogenesis caused by AID repression. In summary, AID is an oncogene that might induce tumorigenesis through DNA demethylation. Targeting AID may represent a novel therapeutic approach to treat metastatic ccRCC.