Evidence map›Paper›PMID 41532422›Full record

ArticleInternational journal of surgery (London, England)2026

S12micro: a machine learning-derived serum miRNA panel for high-performance pan-cancer diagnosis via liquid biopsy.

Zining Long, Chuanfan Zhong, Zitao He, Ruidong Li, Zhenyu Jia, Le Zhang, Shuo Wang, Shanshan Mo, Zhouda Cai, Junhong Deng and 11 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of surgery (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Zining LongDepartment of Andrology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Chuanfan ZhongDepartment of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Zitao HeDepartment of Andrology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Ruidong LiGenetics, Genomics, and Bioinformatics Program, University of California, Riverside, CA, USA.
Zhenyu JiaDepartment of Botany and Plant Sciences, University of California, Riverside, CA, USA.
Le ZhangGenetics, Genomics, and Bioinformatics Program, University of California, Riverside, CA, USA.
Shuo WangDepartment of Andrology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Shanshan MoDepartment of Andrology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Zhouda CaiDepartment of Andrology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Junhong DengDepartment of Andrology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Yuxiang LiangDepartment of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Yangjia ZhuoDepartment of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Yongding WuDepartment of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Yingke LiangDepartment of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Jianheng YeDepartment of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Zhaodong HanDepartment of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Jianjiang XieDepartment of Thoracic Surgery, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Chao CaiDepartment of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangzhou, Guangdong, China.
Xiangming MaoDepartment of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Weide ZhongDepartment of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Jianming LuDepartment of Andrology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.ORCID 0000-0002-3794-641

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer imposes a significant global economic and social burden, emphasizing the urgent need for early diagnostic tools. Although various biomarkers, particularly microRNAs (miRNAs), have been identified, clinical validation remains insufficient. In this study, we characterized a novel serum-based 12-miRNA panel, S12micro, developed through machine learning algorithms for pan-cancer diagnosis. MATERIAL AND

methodsSerum miRNA profiles from 8 public datasets comprising 20 794 samples (13 161 healthy controls and 7633 patients across 13 cancers) were processed with a standardized pipeline. We evaluated 113 pipelines generated from 12 ML algorithms (feature selection + classifier) under strict separation of development and validation. The final operating threshold was fixed on the training cohort and applied unchanged to all validation cohorts. KEGG enrichment was performed on target genes of the 12 miRNAs.

resultsS12micro (12 miRNAs) achieved the highest mean area under the curve (AUC) across large-scale validation datasets (N ≥ 1000; mean AUC = 0.987). In small-scale validation cohorts (N < 1000), S12micro showed AUCs between 0.900 and 1.000 in 31/33 datasets. Beyond ROC analysis, we also evaluated the model using precision-recall curve analysis, decision curve analysis, and the Youden Index. Against previously published regression-specified serum miRNA signatures, S12micro showed superior average performance in the validation datasets. KEGG enrichment analysis revealed enrichment of canonical cancer pathways among predicted targets of S12micro. In-house RT-qPCR feasibility testing across four cancer types supported technical deployability (AUCs ≥ 0.760). Analysis of The Cancer Genome Atlas (TCGA) datasets suggested that these 12 miRNAs are much less abundant in solid tumors than in serum, supporting a serum-oriented diagnostic potential.

conclusionS12micro is a promising, noninvasive serum signature that discriminates cancers from healthy controls across diverse cohorts. While encouraging, real-world specificity versus benign diseases and cross-platform robustness require prospective, multicenter validation with harmonized preanalytics and normalization.

Indexed as

cancer diagnosisliquid biopsymachine learningmiRNAserum

Identifiers

PMID41532422
PMCPMC13105596

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