Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.

Background: Ferroptosis is a newly found non-apoptotic forms of cell death that plays an important role in tumors. However, the prognostic value of ferroptosis-related genes (FRG) in bladder cancer (BLCA) have not been well examined. Methods: FRG data and clinical information were collected from The Cancer Genome Atlas (TCGA). Then, significantly different FRGs were investigated by functional enrichment analyses. The prognostic FRG signature was identified by univariate cox regression and least absolute shrinkage and selection operator (LASSO) analysis, which was validated in TCGA cohort and Gene Expression Omnibus (GEO) cohort. Subsequently, the nomogram integrating risk scores and clinical parameters were established and evaluated. Additionally, Gene Set Enrichment Analyses (GSEA) was performed to explore the potential molecular mechanisms underlying our prognostic FRG signature. Finally, the expression of three key FRGs was verified in clinical specimens. Results: Thirty-two significantly different FRGs were identified from TCGA–BLCA cohort. Enrichment analyses showed that these genes were mainly related to the ferroptosis. Seven genes (TFRC, G6PD, SLC38A1, ZEB1, SCD, SRC, and PRDX6) were then identified to develop a prognostic signature. The Kaplan–Meier analysis confirmed the predictive value of the signature for overall survival (OS) in both TCGA and GEO cohort. A nomogram integrating age and risk scores was established and demonstrated high predictive accuracy, which was validated through calibration curves and receiver operating characteristic (ROC) curve [area under the curve (AUC) = 0.690]. GSEA showed that molecular alteration in the high- or low-risk group was closely associated with ferroptosis. Finally, experimental results confirmed the expression of SCD, SRC, and PRDX6 in BLCA. Conclusion: Herein, we identified a novel FRG prognostic signature that maybe involved in BLCA. It showed high values in predicting OS, and targeting these FRGs may be an alternative for BLCA treatment. Further experimental studies are warranted to uncover the mechanisms that these FRGs mediate BLCA progression.

Objective: To parallelly compare the applicability of the radius, exophytic/endophytic, nearness, anterior/posterior, location nephrometry score (R.E.N.A.L.), the Preoperative Aspects and Dimensions Used for an Anatomical (PADUA), and the centrality index (C‐index) scoring systems in predicting clinical outcomes after partial nephrectomy (PN).Methods: We searched EMBASE, PubMed, Ovid, and Web of Science to perform a meta‐analysis examining the correlation coefficients between three nephrometry scores (NSs) and warm ischemia time (WIT), estimated blood loss (EBL), operation time (OT), length of stay (LOS), and absolute change in eGFR (ACE) up to 25 January 2021.Results: In total, 13 studies including 1496 patients met the criteria for further analysis. Overall, all scoring systems had statistically significant correlations with the WIT, EBL, OT, ACE and LOS and ACE, except for the correlation between PADUA and LOS (r = 0.16 [−0.00, 0.31], p > 0.05). The C‐index had the strongest correlation with WIT (r = −0.35 [−0.43, −0.26], p < 0.05) and ACE (r = −0.29 [−0.48, −0.10], p < 0.05). Weak correlations were observed between OT as well as EBL and each scoring system. Publication bias was observed in PADUA score predicting ACE (p = 0.04) and high heterogeneity was found in some of our results.Conclusion: Until now, this is the first meta‐analysis that parallelly compares these three scoring systems in predicting outcomes after PN. We found that all NSs showed a statistically significant correlation with WIT, EBL, OT, and ACE. Moreover, the C‐index scoring system is the best predictor of WIT and ACE. Due to the existence of publication bias and high heterogeneity, more well‐designed and large‐scale studies are warranted for validation.

Background: Stanniocalcin-1 (STC1) is a well-studied oncogene that promotes different types of cancer progression. However, the expression status of STC1, the values of STC1 on prognosis, and its immune characteristic in bladder cancer (BLCA) have not been well examined. Methods: The expression of STC1 and its clinicopathological as well as immune characteristics in BLCA samples were firstly identified in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Immunohistochemistry (IHC) performed on the tissue microarray (TMA) slide was further used to validate the expression of STC1 and its relationship with immune features in 16 non-muscle invasive bladder cancer (NMIBC) samples and 42 muscle invasive bladder cancer (MIBC) samples. Results: The expression of STC1 was upregulated in higher stage BLCA. High STC1 expression also predicted poor prognosis in BLCA. Subsequently, the TMA validated the expression and prognostic value of STC1 in BLCA. Bioinformatics analysis demonstrated that STC1 and common immune checkpoints as well as immune markers of various immune cells were positively correlated in TCGA. In addition, IHC data from the TMA further validated that tumor cells with higher STC1 level tended to express higher PDL1 as well as increased infiltration of CD3+ T cells. Conclusion: To our knowledge, this is the first comprehensive study that investigates the clinical and immune characteristics of STC1 in BLCA. It may provide new insight into the function of STC1 in regulating tumor immune microenvironment. Further studies are warranted to uncover the potential mechanisms that mediate STC1 expression and tumor immunity in BLCA.

Background: Prostate-specific membrane antigen (PSMA)-targeted 2-(3-{1-carboxy-5-[(6-[18F] fluoro-pyridine-3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid (18F-DCFPyL) positron emission tomography/computed tomography (PET/CT) has shown advantages in primary staging, restaging, and metastasis detection of prostate cancer (PCa). However, little is known about the role of 18F-DCFPyL PET/CT in biochemically recurrent prostate cancer (BRPCa). Hence, we performed a systematic review and meta-analysis to evaluate 18F-DCFPyL PET/CT as first-line imaging modality in early detection of BRPCa.Methods: A comprehensive literature search of PubMed, Web of Science, Embase, and Cochrane Library was conducted until December 2020. The pooled detection rate on a per-person basis and together with 95% confidence interval (CI) was calculated. Furthermore, a prostate-specific antigen (PSA)-stratified performance of detection positivity was obtained to assess the sensitivity of 18F-DCFPyL PET/CT in BRPCa with different PSA levels.Results: A total of nine eligible studies (844 patients) were included in this meta-analysis. The pooled detection rate (DR) of 18F-DCFPyL PET/CT in BRPCa was 81% (95% CI: 76.9–85.1%). The pooled DR was 88.8% for PSA ≥ 0.5 ng/ml (95% CI: 86.2–91.3%) and 47.2% for PSA < 0.5 ng/ml (95% CI: 32.6–61.8%). We also noticed that the regional lymph node was the most common site with local recurrence compared with other sites (45.8%, 95% CI: 42.1–49.6%). Statistical heterogeneity and publication bias were found.Conclusion: The results suggest that 18F-DCFPyL PET/CT has a relatively high detection rate in BRPCa. The results also indicate that imaging with 18F-DCFPyL may exhibit improved sensitivity in BRPCa with increased PSA levels. Considering the publication bias, further large-scale multicenter studies are warranted for validation.

Background: Clear cell renal cell carcinoma (ccRCC) is a prevalent cancer in adult urology, often leading to metastasis and poor prognosis. Recently, advances in tumor immunology and aging research have opened up new possibilities for the treatment of kidney cancer. Therefore, the identification of potential targets and prognostic biomarkers for immunotherapy has become increasingly urgent. Methods: Using GSE168845 data, we identified immune-aging-associated differentially expressed genes (IAR-DEGs) by intersecting differentially expressed immune-related genes and aging-related genes. The prognostic value of IAR-DEGs was determined via univariate and multivariate Cox regression analysis, revealing KL as an independent prognostic factor for ccRCC. We also investigated the correlation between KL and various immune-related factors, including immune cell infiltration, immune score, immune checkpoint, MSI, and TIED score. To confirm the expression of KL in ccRCC, we conducted qRT-PCR assays on both ccRCC cell lines and clinical tissue samples, and compared KL expression levels between normal kidney cell line (HK-2) and ACHN, a ccRCC cell line. Finally, we assessed KL protein expression levels in tissues using immunohistochemistry (IHC). Results: In this study, we utilized Venn diagram analysis to identify 17 co-expressed immune-aging related DEGs from GSE168845, import database, and MSigDB database. GO and KEGG analysis revealed that the functions of the 17 IAR-DEGs were primarily related to “aging”. Univariate and multivariate Cox analysis validated these 17 genes, and KL was determined to be an independent prognostic factor for ccRCC. The downregulation of KL was observed in ccRCC tissues and was negatively associated with T stage, M stage, pathological stage, and histologic grade (p < 0.05). This downregulation indicated disease deterioration and a shortened overall survival period. Our calibration curves and nomogram demonstrated the excellent predictive potential of KL. GSEA analysis showed that KL gene mediated immune and aging-related pathways, and was significantly correlated with immune infiltration and MS and TIED score. More research has revealed a significant reduction in KL mRNA expression levels in human renal cancer cells, particularly in ccRCC tissues compared to adjacent normal kidney tissues. Moreover, immunohistochemistry data have demonstrated a marked decrease in KL protein expression levels in ccRCC cells when compared to adjacent normal tissues. Conclusions: KL was a potential aging-related target for immunotherapy and valid prognostic biomarker for ccRCC patients.