Growing evidence has highlighted the immune response as an important feature of carcinogenesis and therapeutic efficacy in clear cell renal cell carcinoma (ccRCC). This study categorized ccRCC cases into high and low score groups based on their immune/stromal scores generated by the ESTIMATE algorithm, and identified an association between these scores and prognosis. Differentially expressed tumor environment (TME)-related genes extracted from common upregulated components in immune and stromal scores were described using functional annotations and protein–protein interaction (PPI) networks. Most PPIs were selected for further prognostic investigation. Many additional previously neglected signatures, including AGPAT9, AQP7, HMGCS2, KLF15, MLXIPL, PPARGC1A, exhibited significant prognostic potential. In addition, multivariate Cox analysis indicated that MIXIPL and PPARGC1A were the most significant prognostic signatures, and were closely related to immune infiltration in TCGA cohort. External prognostic validation of MIXIPL and PPARGC1A was undertaken in 380 ccRCC cases from a real-world cohort. These findings indicate the relevance of monitoring and manipulation of the microenvironment for ccRCC prognosis and precision immunotherapy.

Summary Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest—namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial—ENTHUSE M1—in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39–4·62, p<0·0001; reference model: 2·56, 1·85–3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified...

Background Anemia is one of the most common complications of sepsis. Sepsis-related anemia is associated mainly with inflammation. We aimed to observe the changes in the inflammatory anemia-associated parameters of patients with sepsis in the early stage of intensive care unit (ICU) admission and to evaluate their association with 28-day mortality. Methods A total of 198 patients with sepsis were divided into survivor ( n = 110) and non-survivor ( n = 88) groups on the basis of 28-day survival. Healthy volunteers ( n = 20) were enrolled as a control group. Plasma levels of iron, ferritin, erythropoietin (EPO), soluble transferrin receptor (sTfR), hepcidin, interleukin-6 (IL-6), hemoglobin and the red blood cell distribution width (RDW) were measured on days 1, 3 and 7 of ICU admission. Clinical data and laboratory findings were collected, and the Sequential Organ Failure Assessment (SOFA) score was calculated. Results Patients with sepsis showed significant decreases in hemoglobin, plasma iron and sTfR/log ferritin and significant increases in plasma EPO, sTfR, hepcidin, ferritin and IL-6 on days 1, 3 and 7 of ICU admission compared with healthy volunteers. Hemoglobin was correlated negatively with plasma IL-6 and hepcidin. In patients with sepsis, non-survivors had significantly lower plasma iron, EPO and sTfR/log ferritin, but higher plasma hepcidin, ferritin and IL-6 than survivors on days 1, 3 and 7 of ICU admission. Plasma EPO, hepcidin, ferritin, IL-6, sTfR/log ferritin, the RDW and SOFA score were associated significantly with 28-day mortality but to a varying extent. In particular, in predicting 28-day mortality, plasma hepcidin had an area under the receiver operating curve of 0.808 and 87.3% specificity, which was the highest among the inflammatory anemia-associated parameters tested. Conclusions Inflammatory anemia-associated parameters changed significantly in patients with sepsis in the first week of ICU admission. Plasma EPO, hepcidin, ferritin, IL-6, sTfR/log ferritin, the RDW and SOFA score were associated significantly with 28-day mortality. Plasma hepcidin might have a superior predictive value, with high specificity, compared with other inflammatory anemia-associated parameters for 28-day mortality of sepsis patients in the ICU. Electronic supplementary material The online version of this article (10.1186/s13613-019-0542-7) contains supplementary material, which is available to authorized users.

Compared with single biomarker, the multiplex model including PCA3, TMPRSS2: ERG, Annexin A3 and Sarcosine adds even more to the diagnostic performance for predicting CaP. Further validation experiments and optimization for the strategy of constructing this model are warranted.

Micro (mi) RNAs are important regulators involved in various physical and pathological processes, including cancer. The miRNA-302 family has been documented as playing a critical role in carcinogenesis. In this study, we investigated the role of miRNA-302a in prostate cancer (PCa). MiRNA-302a expression was detected in 44 PCa tissues and 10 normal prostate tissues, and their clinicopathological significance was analyzed. Cell proliferation and cell cycle analysis were performed on PCa cells that stably expressed miRNA-302a. The target gene of miRNA-302a and the downstream pathway were further investigated. Compared with normal prostate tissues, miRNA-302a expression was downregulated in PCa tissues, and was even lower in PCa tissues with a Gleason score ≥8. Overexpression of miRNA-302a induced G1/S cell cycle arrest in PCa cells, and suppressed PCa cell proliferation both in vitro and in vivo. Furthermore, miRNA-302a inhibits AKT expression by directly binding to its 3΄ untranslated region, resulting in subsequent alterations of the AKT-GSK3β-cyclin D1 and AKT-p27Kip1 pathway. These results reveal miRNA-302a as a tumor suppressor in PCa, suggesting that miRNA-302a may be used as a potential target for therapeutic intervention in PCa.

The skeleton is the most common metastatic organ in patients with prostate cancer (PCa). Non-invasive biomarkers that can facilitate the detection and monitoring of bone metastases are highly desirable. We designed this study to assess the expression patterns of serum miR-141 in patients with bone-metastatic PCa. Serum samples were collected to measure the miR-141 level in 56 patients, including six with benign prostatic hyperplasia (BPH), 20 with localized PCa and 30 with bone-metastatic PCa (10 with hormone-naive PCa, 10 with hormone-sensitive PCa and 10 with hormone-refractory PCa). A bone scan was performed for each patient with PCa to assess the number of bone lesions. The quantification of serum miR-141 levels was assayed by specific TaqMan qRT-PCR. The results showed that serum miR-141 levels were elevated in patients with bone metastasis (P,0.001). There was no statistically significant difference in the serum miR-141 levels between patients with BPH and patients with localized PCa. Using Kendall's bivariate correlation test, both the Gleason score and the number of bone-metastatic lesions were found to correlate with serum miR-141 levels (P50.012 and P,0.001, respectively). The serum miR-141 level was found to be positively correlated with alkaline phosphatase (ALP) level in patients with skeletal metastasis, using Pearson's bivariate correlation test. No relationship was found between the serum miR-141 level and the serum prostate-specific antigen (PSA) level. We concluded that serum miR-141 levels are elevated in patients with bone-metastatic PCa and that patients with higher levels of serum miR-141 developed more bone lesions. Furthermore, serum miR-141 levels are correlated with serum ALP levels but not serum PSA levels.

Background: Growing evidence has demonstrated immune reactivity as a confirmed important carcinogenesis and therapy efficacy for clear cell renal cell carcinoma (ccRCC). Aquaporin 9 (AQP9) is involved in many immune-related signals; however, its role in ccRCC remains to be elucidated. This study investigated AQP9 expression in tumor tissues and defined the prognostic value in ccRCC patients. Methods: A total of 913 ccRCC patients with available RNA-sequence data from the Cancer Genome Atlas (TCGA) database and Fudan University Shanghai Cancer Center (FUSCC) were consecutively recruited in analyses. Differential transcriptional and proteome expression profiles were obtained and validated using multiple datasets. A partial likelihood test from Cox regression analysis was developed to address the influence of independent factors on progression-free survival (PFS) and overall survival (OS). The Kaplan-Meier method and log-rank test were performed to assess survival. Receiver operating characteristic (ROC) curves were used to describe binary classifier value of AQP9 using area under the curve (AUC) score. Functional enrichment analyses and immune infiltration analysis were used to describe significantly involved hallmark pathways of hub genes. Results: Significantly elevated transcriptional and proteomic AQP9 expressions were found in ccRCC samples. Increased AQP9 mRNA expression was significantly associated with advanced clinicopathological parameters and correlated with shorter PFS and OS in TCGA and FUSCC cohorts (p < 0.001). ROC curves suggested the significant diagnostic and prognostic ability of AQP9 (PFS, AUC = 0.823; OS, AUC = 0.828). Functional annotations indicated that AQP9 is involved in the most significant hallmarks including complement, coagulation, IL6/JAK-STAT3, inflammatory response and TNF-alpha signaling pathways. Conclusion: Our study revealed that elevated AQP9 expression was significantly correlated with aggressive progression, poor survival and immune infiltrations in ccRCC patients, and we validated its prognostic value in a real-world cohort. These data suggest that AQP9 may act as an oncogene and a promising prognostic marker in ccRCC.

Controversial data on sarcosine as a promising biomarker for prostate cancer (PCa) detection are present. The objective was to clarify these discrepancies and reevaluate the potential value of sarcosine in PCa. Sarcosine algorithms (supernatant and sediment sarcosine/creatinine, supernatant and sediment log2 (sarcosine/alanine)) in urine samples from 71 untreated patients with PCa, 39 patients with no evidence of malignancy (NEM) and 20 healthy women and men were quantified by liquid chromatography/tandem mass spectrometry. Although any sarcosine algorithms were significantly higher in PCa patients than in NEM patients (all Po0.05), comparable sarcosine values were measured in healthy women and men. Additionally, neither biopsy Gleason score nor clinical T-stage were correlated with sarcosine algorithms (all P40.05), and receiver operating characteristic curve analysis indicated that the diagnostic power of any of sarcosine algorithms was nonsignificantly higher than that of serum and urine PSA, but nonsignificantly lower than prostate cancer antigen 3 (PCA3) and the percent-free PSA (%fPSA). Improved diagnostic performances were observed when any of sarcosine algorithms was combined with PCA3 or %fPSA. In conclusion, the predictive power of sarcosine in PCa is modest compared with PCA3 and %fPSA. Sarcosine, which awaits more validation before it reaches the clinic, could be included into the list of candidate PCa biomarkers.