Both 5-methylcytosine (5mC) and its oxidized form 5-hydroxymethylcytosine (5hmC) have been proposed to be involved in tumorigenesis. Because the readout of the broadly used 5mC mapping method, bisulfite sequencing (BS-seq), is the sum of 5mC and 5hmC levels, the 5mC/5hmC patterns and relationship of these two modifications remain poorly understood. By profiling real 5mC (BS-seq corrected by Tet-assisted BS-seq, TAB-seq) and 5hmC (TAB-seq) levels simultaneously at single-nucleotide resolution, we here demonstrate that there is no global loss of 5mC in kidney tumors compared with matched normal tissues. Conversely, 5hmC was globally lost in virtually all kidney tumor tissues. The 5hmC level in tumor tissues is an independent prognostic marker for kidney cancer, with lower levels of 5hmC associated with shorter overall survival. Furthermore, we demonstrated that loss of 5hmC is linked to hypermethylation in tumors compared with matched normal tissues, particularly in gene body regions. Strikingly, gene body hypermethylation was significantly associated with silencing of the tumor-related genes. Downregulation of IDH1 was identified as a mechanism underlying 5hmC loss in kidney cancer. Restoring 5hmC levels attenuated the invasion capacity of tumor cells and suppressed tumor growth in a xenograft model. Collectively, our results demonstrate that loss of 5hmC is both a prognostic marker and an oncogenic event in kidney cancer by remodeling the DNA methylation pattern.

Autophagy is primarily an efficient intracellular catabolic pathway used for degradation of abnormal cellular protein aggregates and damaged organelles. Although autophagy was initially proposed to be a cellular stress responder, increasing evidence suggests that it carries out normal physiological roles in multiple biological processes. To date, autophagy has been identified in most organs and at many different developmental stages, indicating that it is not only essential for cellular homeostasis and renovation, but is also important for organ development. Herein, we summarize our current understanding of the functions of autophagy (which here refers to macroautophagy) in the mammalian life cycle.

Biochemical recurrence (BCR) occurs in up to 27% of patients after radical prostatectomy (RP) and often compromises oncologic survival. To determine whether imaging signatures on clinical prostate magnetic resonance imaging (MRI) could noninvasively characterize biochemical recurrence and optimize treatment. We retrospectively enrolled 485 patients underwent RP from 2010 to 2017 in three institutions. Quantitative and interpretable features were extracted from T2 delineated tumors. Deep learning-based survival analysis was then applied to develop the deep-radiomic signature (DRS-BCR). The model’s performance was further evaluated, in comparison with conventional clinical models. The model achieved C-index of 0.802 in both primary and validating cohorts, outweighed the CAPRA-S score (0.677), NCCN model (0.586) and Gleason grade group systems (0.583). With application analysis, DRS-BCR model can significantly reduce false-positive predictions, so that nearly one-third of patients could benefit from the model by avoiding overtreatments. The deep learning-based survival analysis assisted quantitative image features from MRI performed well in prediction for BCR and has significant potential in optimizing systemic neoadjuvant or adjuvant therapies for prostate cancer patients.

Background Signal transducer and activator of transcription 3 (STAT3) has been shown to upregulate gene transcription during tumorigenesis. However, how STAT3 initiates transcription remains to be exploited. This study is to reveal the role of CREPT (cell cycle-related and elevated-expression protein in tumours, or RPRD1B) in promoting STAT3 transcriptional activity. Methods BALB/c nude mice, CREPT overexpression or deletion cells were employed for the assay of tumour formation, chromatin immunoprecipitation, assay for transposase-accessible chromatin using sequencing. Results We demonstrate that CREPT, a recently identified oncoprotein, enhances STAT3 transcriptional activity to promote tumorigenesis. CREPT expression is positively correlated with activation of STAT3 signalling in tumours. Deletion of CREPT led to a decrease, but overexpression of CREPT resulted in an increase, in STAT3-initiated tumour cell proliferation, colony formation and tumour growth. Mechanistically, CREPT interacts with phosphorylated STAT3 (p-STAT3) and facilitates p-STAT3 to recruit p300 to occupy at the promoters of STAT3-targeted genes. Therefore, CREPT and STAT3 coordinately facilitate p300-mediated acetylation of histone 3 (H3K18ac and H3K27ac), further augmenting RNA polymerase II recruitment. Accordingly, depletion of p300 abolished CREPT-enhanced STAT3 transcriptional activity. Conclusions We propose that CREPT is a co-activator of STAT3 for recruiting p300. Our study provides an alternative strategy for the therapy of cancers related to STAT3.

Rationale: To reduce upgrading and downgrading between needle biopsy (NB) and radical prostatectomy (RP) by predicting patient-level Gleason grade groups (GGs) of RP to avoid over- and under-treatment. Methods: In this study, we retrospectively enrolled 575 patients from two medical institutions. All patients received prebiopsy magnetic resonance (MR) examinations, and pathological evaluations of NB and RP were available. A total of 12,708 slices of original male pelvic MR images (T2-weighted sequences with fat suppression, T2WI-FS) containing 5405 slices of prostate tissue, and 2,753 tumor annotations (only T2WI-FS were annotated using RP pathological sections as ground truth) were analyzed for the prediction of patient-level RP GGs. We present a prostate cancer (PCa) framework, PCa-GGNet, that mimics radiologist behavior based on deep reinforcement learning (DRL). We developed and validated it using a multi-center format. Results: Accuracy (ACC) of our model outweighed NB results (0.815 [95% confidence interval (CI): 0.773-0.857] vs. 0.437 [95% CI: 0.335-0.539]). The PCa-GGNet scored higher (kappa value: 0.761) than NB (kappa value: 0.289). Our model significantly reduced the upgrading rate by 27.9% ( P < 0.001) and downgrading rate by 6.4% ( P = 0.029). Conclusions: DRL using MRI can be applied to the prediction of patient-level RP GGs to reduce upgrading and downgrading from biopsy, potentially improving the clinical benefits of prostate cancer oncologic controls.