Development of machine learning prognostic models for overall survival of prostate cancer patients with lymph node-positive.

Prostate cancer (PCa) patients with lymph node involvement (LNI) constitute a single-risk group with varied prognoses. Existing studies on this group have focused solely on those who underwent prostatectomy (RP), using statistical models to predict prognosis. This study aimed to develop an easily accessible individual survival prediction tool based on multiple machine learning (ML) algorithms to predict survival probability for PCa patients with LNI. A total of 3280 PCa patients with LNI were identified from the Surveillance, Epidemiology, and End Results (SEER) database, covering the years 2000-2019. The primary endpoint was overall survival (OS). Gradient Boosting Survival Analysis (GBSA), Random Survival Forest (RSF), and Extra Survival Trees (EST) were used to develop prognosis models, which were compared to Cox regression. Discrimination was evaluated using the time-dependent areas under the receiver operating characteristic curve (time-dependent AUC) and the concordance index (c-index). Calibration was assessed using the time-dependent Brier score (time-dependent BS) and the integrated Brier score (IBS). Moreover, the beeswarm summary plot in SHAP (SHapley Additive exPlanations) was used to display the contribution of variables to the results. The 3280 patients were randomly split into a training cohort (n = 2624) and a validation cohort (n = 656). Nine variables including age at diagnosis, race, marital status, clinical T stage, prostate-specific antigen (PSA) level at diagnosis, Gleason Score (GS), number of positive lymph nodes, radical prostatectomy (RP), and radiotherapy (RT) were used to develop models. The mean time-dependent AUC for GBSA, RSF, and EST was 0.782 (95% confidence interval [CI] 0.779-0.783), 0.779 (95% CI 0.776-0.780), and 0.781 (95% CI 0.778-0.782), respectively, which were higher than the Cox regression model of 0.770 (95% CI 0.769-0.773). Additionally, all models demonstrated almost similar calibration, with low IBS. A web-based prediction tool was developed using the best-performing GBSA, which is accessible at https://pengzihexjtu-pca-n1.streamlit.app/ . ML algorithms showed better performance compared with Cox regression and we developed a web-based tool, which may help to guide patient treatment and follow-up.

The active phthalate metabolite, DHEP, induces proliferation and metastasis of prostate cancer cells via upregulation of ß-catenin and downregulation of KLF7.

Phthalates such as DHEP are among the widely used compounds in industry. It has been shown that DHEP can convey various biological consequences in mammalian cells, among them, the carcinogenic effects of DHEP are emphasized. The present study aimed to assess the impact of DHEP exposure on the proliferation and invasiveness of DU145 prostate cancer cells through in vitro and in vivo models. The DU145 cells were treated with increasing concentrations of DHEP and the tumorigenic parameters were analyzed. KLF7 as a probable mediator of the effect of DHEP was either overexpressed or knocked down in DU145 to evaluate the probable impact of KLF7 on the biological effects of DHEP. The effect of DHEP was also studied in a DU145 xenograft tumor model. The moderate doses of DHEP increased the proliferation and migration of DU145 cells. In the case of gene expression patterns, DHEP reduced the levels of p53 and KLF7 while elevated the expression of ß-catenin. The knock-down of KLF7 conveyed comparable effects to that of DHEP to some degree and increased the proliferation of DU145 cells, while the transduction of KLF7 increased the expressions of p53 and p21 along with controlling the tumor size. The present study demonstrated the potential of DHEP in increasing the tumorigenic properties of DU145 cells along with a focus on the underlying mechanisms. Sustained exposure to DHEP can cause a dysregulation in balance between oncogenes and tumor suppressor genes which is the hallmark of malignant transformation. Thus, special considerations seem necessary for the safe exploitation of phthalates.

Corrigendum: A New Prognostic Risk Score: Based on the Analysis of Autophagy-Related Genes and Renal Cell Carcinoma.

[This corrects the article DOI: 10.3389/fgene.2021.820154.].

A New Prognostic Risk Score: Based on the Analysis of Autophagy-Related Genes and Renal Cell Carcinoma.

Introduction: Clear cell renal cell carcinoma (ccRCC) patients suffer from its high recurrence and metastasis rate, and a new prognostic risk score to predict individuals with high possibility of recurrence or metastasis is in urgent need. Autophagy has been found to have a dual influence on tumorigenesis. In this study we aim to analyze autophagy related genes (ATGs) and ccRCC patients and find a new prognostic risk score. Method: Analyzing differential expression genes (DEGs) in TCGA-KIRC dataset, and took intersection with ATGs. Through lasso, univariate, and multivariate cox regression, DEGs were chosen, and the coefficients and expression levels of them were components constructing the formula of risk score. We analyzed mRNA expression of DEGs in tumor and normal tissue in ONCOMINE database and TCGA-KIRC dataset. The Human Protein Atlas (HPA) was used to analyze protein levels of DEGs. The protein-protein interaction (PPI) network was examined in STRING and visualized in cytoscape. Functional enrichment analysis was performed in RStudio. To prove the ability and practicibility of risk score, we analyzed univariate and multivariate cox regression, Kaplan-Meier curve (K-M curve), risk factor association diagram, receiver operating characteristic curve (ROC curve) of survival and nomogram, and the performance of nomogram was evaluated by calibration curve. Then we further explored functional enrichment related to risk groups through Gene Set Enrichment Analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and Metascape database. At last, we investigated immune cell infiltration of DEGs and two risk groups through TIMER database and "Cibersort" algorithm. Result: We identified 7 DEGs (BIRC5, CAPS, CLDN7, CLVS1, GMIP, IFI16, and TCIRG1) as components of construction of risk score. All 7 DEGs were differently expressed in ccRCC and normal tissue according to ONCOMINE database and TCGA-KIRC dataset. Functional enrichment analysis indicated DEGs, and their most associated genes were shown to be abundant in autophagy-related pathways and played roles in tumorigenesis and progression processes. A serious analysis proved that this risk score is independent from the risk signature of ccRCC patients. Conclusion: The risk score constructed by 7 DEGs had the ability of predicting prognosis of ccRCC patients and was conducive to the identification of novel prognostic molecular markers. However, further experiment is still needed to verify its ability and practicability.

Regulation of nonylphenol-induced reproductive toxicity in mouse spermatogonia cells by miR-361-3p.

Nonylphenol (NP) is an environmental chemical that affects apoptosis and male infertility. In our study, we found that a high concentration of NP could down-regulate the expression of microRNA-361-3p (miR-361-3p) in the murine GC-1 spermatogonia cell line and in vivo in murine spermatogonia. Additionally, one direct target of this miR, the 3' untranslated region of Killin (Klln) mRNA, was identified. Klln encodes a transcription factor that directly regulates the expression of Tp73 (transcriptionally active p73), whose encoded protein can up-regulate the expression of Puma (p53 upregulated modulator of apoptosis). Thus, our investigation revealed that the expression of Klln, Tp73, and Puma increased upon NP-dependent down-regulation of miR-361-3p, which eventually leads to apoptosis of spermatogonia.