ArticleBMC urology2025
Role of APOC1 and NOP16 in the diagnosis of prostate cancer.
Article in BMC urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundPCa (Prostate cancer) is the most prevalent urogenital malignancy among men. The genes APOC1(Apolipoprotein C1) and NOP16 (Nucleolar protein 16) have been associated with various types of cancer. This article aims to investigate the potential of APOC1 and NOP16 as preliminary proactive diagnostic markers for PCa.
methodsSearch for APOC1 and NOP16 using the TCGA (The Cancer Genome Atlas) and GEPIA (Gene Expression Profiling Interactive Analysis) databases. Immunohistochemical staining was employed to detect the expression of APOC1 and NOP16 in prostate tissue. The enzyme-linked immunosorbent assay (ELISA) was utilized to quantify the levels of APOC1 and NOP16 proteins. Spearman correlation coefficient and Pearson correlation coefficient were calculated to validate the association between APOC1 and NOP16. A binary logistic regression model was developed to analyze the factors that influence PCa. Predictive models for APOC1 and NOP16 and PSA, and their role in detecting PCa, were established through the construction of receiver operating characteristic (ROC) curves.
resultsAPOC1 and NOP16 are up-regulated in PCa tissues, and their expressions are related with the clinical stage of PCa(n = 51). Furthermore, compared with individuals affected by benign prostatic hyperplasia (BPH), the expression levels of APOC1 and NOP16 in the serum of individuals affected by PCa are higher(n = 61). Moreover, in the tissues and serum of the same PCa patient, APOC1 and NOP16 are positively correlated(n = 32). APOC1 and NOP16 are independent influencing factors for PCa(n = 50). APOC1 and NOP16 showed predictive potential for PCa (n = 50, APOC1 AUC = 0.729, NOP16 AUC = 0.777).
conclusionsAPOC1 and NOP16 are anticipated to function as biomarkers for the early proactive diagnosis of PCa.
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.