SynthesisProstate cancer and prostatic diseases2022
The promising role of new molecular biomarkers in prostate cancer: from coding and non-coding genes to artificial intelligence approaches.
Synthesis in Prostate cancer and prostatic diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.
- A systematic review and meta-analysis of the prevalence and risk factors of prostate cancer in Nigeria.BMC cancer · 2025Pooled it
- Circulating Tumor Cell Count and Overall Survival in Patients With Metastatic Hormone-Sensitive Prostate Cancer.JAMA network open · 2024Trial
- Present status of prostate cancer diagnosis, limitations, challenges, and future endeavors.Annals of medicine · 2026Review
- Integrative Bioinformatics and Machine Learning Analysis Identifies Novel Molecular Biomarkers in Prostate Adenocarcinoma.International journal of molecular sciences · 2026Article
- The regulatory mechanism and clinical significance of RNA editing in prostate cancer.Current urology · 2026Review
- Review
- Evolving Treatments and Resistance Mechanisms in Prostate Cancer Therapeutics.ACS pharmacology & translational science · 2026Review
- Multifocal cohort analysis unveils cell types associated with regional lymph node seeding in prostate cancer.Genome medicine · 2026Article
- Electrochemical Biosensors for Cancer Diagnosis and Prognosis Using Protein Biomarkers: Current Trends, Advances, and Clinical Translation Potential.Sensors (Basel, Switzerland) · 2026Review
- Article
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- A Neuroendocrine Differentiation-related Molecular Model for Prognosis Prediction in Prostate Cancer Patients.Current medicinal chemistry · 2026Article
- Prostate cancer diagnosis using sensitive and sophisticated machine learning classifiers based on non-invasive urinary RNA biomarkers (PCASSO).Scientific reports · 2025Article
- Investigation of the association of tRNA-derived fragments (tRF-17-79MP9PP and tRF-18-79MP9P04) with prostate cancer.Molecular biology reports · 2025Article
- Review
- Ki67 and TNFRII as Potential Clinical Markers for Effective Clinical Staging of Advanced Prostate Cancer.Cancers · 2025Article
- Metabolic fingerprinting enables rapid, label-free histopathology in gastric cancer diagnosis and prognostic prediction.Cell reports. Medicine · 2025Article
- Accuracy, readability, and understandability of large language models for prostate cancer information to the public.Prostate cancer and prostatic diseases · 2025Article
- Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRisk stratification or progression in prostate cancer is performed with the support of clinical-pathological data such as the sum of the Gleason score and serum levels PSA. For several decades, methods aimed at the early detection of prostate cancer have included the determination of PSA serum levels. The aim of this systematic review is to provide an overview about recent advances in the discovery of new molecular biomarkers through transcriptomics, genomics and artificial intelligence that are expected to improve clinical management of the prostate cancer patient.
methodsAn exhaustive search was conducted by Pubmed, Google Scholar and Connected Papers using keywords relating to the genetics, genomics and artificial intelligence in prostate cancer, it includes "biomarkers", "non-coding RNAs", "lncRNAs", "microRNAs", "repetitive sequence", "prognosis", "prediction", "whole-genome sequencing", "RNA-Seq", "transcriptome", "machine learning", and "deep learning".
resultsNew advances, including the search for changes in novel biomarkers such as mRNAs, microRNAs, lncRNAs, and repetitive sequences, are expected to contribute to an earlier and accurate diagnosis for each patient in the context of precision medicine, thus improving the prognosis and quality of life of patients. We analyze several aspects that are relevant for prostate cancer including its new molecular markers associated with diagnosis, prognosis, and prediction to therapy and how bioinformatic approaches such as machine learning and deep learning can contribute to clinic. Furthermore, we also include current techniques that will allow an earlier diagnosis, such as Spatial Transcriptomics, Exome Sequencing, and Whole-Genome Sequencing.
conclusionTranscriptomic and genomic analysis have contributed to generate knowledge in the field of prostate carcinogenesis, new information about coding and non-coding genes as biomarkers has emerged. Synergies created by the implementation of artificial intelligence to analyze and understand sequencing data have allowed the development of clinical strategies that facilitate decision-making and improve personalized management in prostate cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.