ArticleBiomarkers in medicine2025
Integrating cell-free DNA methylation of SEPT9 and SFRP2 into a machine learning model for early diagnosis of HCC.
Article in Biomarkers in medicine, 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHepatocellular carcinoma (HCC), a primary contributor to cancer-associated mortality, necessitates enhanced early detection. This study evaluated machine learning models that merge methylated SEPTIN9 (SEPT9) and secreted frizzled-related protein 2 (SFRP2) within circulating cell-free DNA (cfDNA) to detect HCC.
methodsA cohort of 165 healthy volunteers, 24 precancerous patients of HCC and 112 HCC patients were divided into training and validation sets. Methylated SEPT9 and SFRP2 (mSEPT9/mSFRP2) were detected using real-time PCR. Based on those methylation biomarkers and/or conventional biomarkers (CEA, AFP, CA125, and CA19-9), six machine learning algorithms, including Random Forest (RF), were employed to establish models for the training set. Models were evaluated for area under the ROC curve (AUC), sensitivity, and specificity, and subsequently validated in the validation set.
resultsThe RF model outperformed other models. In training, it achieved an AUC of 0.834 (95% CI: 0.745-0.923), exhibiting 69.3% sensitivity and 80.6% specificity for the methylation-specific signature group (mSS group: mSEPT9/mSFRP2). In validation, the RF model for the mSS group showed an AUC of 0.865 (95% CI: 0.811-0.946), with 85.4% sensitivity and 71.4% specificity.
conclusionsThe RF-based model integrating mSEPT9/mSFRP2 in cfDNA can be a promising approach for HCC diagnosis.
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.