ArticleHeliyon2023
Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Article
- Advances in the identification of novel cell signatures in benign prostatic hyperplasia and prostate cancer using single-cell RNA sequencing.Frontiers in immunology · 2025Review
- Challenges and opportunities for statistical power and biomarker identification arising from rhythmic variation in proteomics.Npj biological timing and sleep · 2025Article
- Dynamic Soluble IL-6R/Soluble gp130 Ratio as a Potential Indicator for the Prostate Malignancy Phenotype-A Multicenter Case-Control Study.Journal of personalized medicine · 2024Article
- A Comprehensive Review of Protein Biomarkers for Invasive Lung Cancer.Current oncology (Toronto, Ont.) · 2024Review
- Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data.BMC medical informatics and decision making · 2024Article
- Biomarker Identification through Proteomics in Colorectal Cancer.International journal of molecular sciences · 2024Review
- Correlation between Selenium and Zinc Levels and Survival among Prostate Cancer Patients.Nutrients · 2024Article
- Immune-related diagnostic markers for benign prostatic hyperplasia and their potential as drug targets.Frontiers in immunology · 2024Article
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 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is an unmet need for improved diagnostic testing and risk prediction for cases of prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum proteome and lipidome of 262 study participants by liquid chromatography-mass spectrometry, including participants diagnosed with PCa, benign prostatic hyperplasia (BPH), or otherwise healthy volunteers, with the aim of improving biomarker specificity. Although a two-class machine learning model separated PCa from controls with sensitivity of 0.82 and specificity of 0.95, adding BPH resulted in a statistically significant decline in specificity for prostate cancer to 0.76, with half of BPH cases being misclassified by the model as PCa. A small number of biomarkers differentiating between BPH and prostate cancer were identified, including proteins in MAP Kinase pathways, as well as in lipids containing oleic acid; these may offer a route to greater specificity. These results highlight, however, that whilst there are opportunities for machine learning, these will only be achieved by use of appropriate training sets that include confounding comorbidities, especially when calculating the specificity of a test.
Indexed as
Identifiers
What OpenQuestion holds
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.