Disulfidptosis-related genes serve as potential prognostic biomarkers and indicate tumor microenvironment characteristics and immunotherapy response in prostate cancer.

Disulfidptosis, a newly identified programmed cell death pathway in prostate cancer (PCa), is closely associated with intracellular disulfide stress and glycolysis. This study aims to elucidate the roles of disulfidptosis-related genes (DRGs) in the pathogenesis and progression of PCa, with the goal of improving diagnostic and therapeutic approaches. We analyzed PCa datasets and normal tissue transcriptome data from TCGA, GEO, and MSKCC.

Explainable and visualizable machine learning models to predict biochemical recurrence of prostate cancer.

Purpose: Machine learning (ML) models presented an excellent performance in the prognosis prediction. However, the black box characteristic of ML models limited the clinical applications. Here, we aimed to establish explainable and visualizable ML models to predict biochemical recurrence (BCR) of prostate cancer (PCa).

Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases.

Artificial intelligence for the diagnosis of clinically significant prostate cancer based on multimodal data: a multicenter study.

Background: The introduction of multiparameter MRI and novel biomarkers has greatly improved the prediction of clinically significant prostate cancer (csPCa). However, decision-making regarding prostate biopsy and prebiopsy examinations is still difficult. We aimed to establish a quick and economic tool to improve the detection of csPCa based on routinely performed clinical examinations through an automated machine learning platform (AutoML).

Establishment of a risk score model for bladder urothelial carcinoma based on energy metabolism-related genes and their relationships with immune infiltration.

Bladder urothelial carcinoma (BLCA) is a common malignant tumor of the human urinary system, and a large proportion of BLCA patients have a poor prognosis. Therefore, there is an urgent need to find more efficient and sensitive biomarkers for the prognosis of BLCA patients in clinical practice. RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas, and 584 energy metabolism-related genes (EMRGs) were obtained from the Reactome pathway database.

Urinary exosomal prostate-specific antigen is a noninvasive biomarker to detect prostate cancer: Not only old wine in new bottles.

The study aimed at evaluating the performance of urinary exosomal prostate-specific antigen (UE-PSA) to predict the results of initial prostate biopsies and discriminate clinically significant prostate cancer (Gleason score ≥ 7, csPCa) from nonsignificant PCa (Gleason score < 7, nsPCa) plus benign patients. Two hundred seventy-two consecutive participants were admitted who underwent a prostate biopsy. The UE-PSA expression was detected by enzyme-linked immunosorbent assay (ELISA).

Long noncoding RNA as a potential diagnostic tool for prostate cancer: a systematic review and meta-analysis.

Purpose: To identify consistently expressed lncRNAs and suitable lncRNAs with high sensitivity and specificity from multiple independent studies as potential biomarkers for PCa diagnostics.

Methods: We searched multiple electronic databases including PubMed, Web of Science, EMBASE, Cochrane Library, CNKI, CQVIP, Wanfang, and CBMdisc for studies published up to July 2022. The quality of the included studies was assessed by two independent reviewers based on the QUADAS-2 tool using Review Manager 5.

Circulating exosomal mRNA signatures for the early diagnosis of clear cell renal cell carcinoma.

Background: There are no proven tumor biomarkers for the early diagnosis of clear cell renal cell carcinoma (ccRCC) thus far. This study aimed to identify novel biomarkers of ccRCC based on exosomal mRNA (emRNA) profiling and develop emRNA-based signatures for the early detection of ccRCC.

Methods: Four hundred eighty-eight participants, including 226 localized ccRCCs, 73 patients with benign renal masses, and 189 healthy controls, were recruited.

miR-224 is an early-stage biomarker of hepatocellular carcinoma with miR-224 and miR-125b as prognostic biomarkers.

The aim was to systematically investigate the miRNA biomarkers for early diagnosis of hepatocellular carcinoma (HCC). A systematic review and meta-analysis of miRNA expression in HCC were performed.  A total of 4903 cases from 30 original studies were comprehensively analyzed.

RNA sequencing of plasma exosomes revealed novel functional long noncoding RNAs in hepatocellular carcinoma.

Exosomal long noncoding RNA (lncRNA) has been found to be associated with the development of cancers. However, the expression characteristics and the biological roles of exosomal lncRNAs in hepatocellular carcinoma (HCC) remain unknown. Here, by RNA sequencing, we found 9440 mRNAs and 8572 lncRNAs were differentially expressed (DE-) in plasma exosomes between HCC patients and healthy controls.

Exosomal miR-1246 in body fluids is a potential biomarker for gastrointestinal cancer.

Aim: The aim was to systematically evaluate whether exosomal miRNAs could be regarded as potential minimally invasive biomarkers of diagnosis for gastrointestinal cancer.

Methods: A systematic review and meta analysis of exosomal miRNA expression in gastrointestinal cancer were performed.

Results: A total of 370 articles were retrieved from PubMed and EMBASE.

Understanding the complexity of temperature dynamics in Xinjiang, China, from multitemporal scale and spatial perspectives.

Based on the observed data from 51 meteorological stations during the period from 1958 to 2012 in Xinjiang, China, we investigated the complexity of temperature dynamics from the temporal and spatial perspectives by using a comprehensive approach including the correlation dimension (CD), classical statistics, and geostatistics. The main conclusions are as follows (1) The integer CD values indicate that the temperature dynamics are a complex and chaotic system, which is sensitive to the initial conditions. (2) The complexity of temperature dynamics decreases along with the increase of temporal scale.