Evidence map›Paper›PMID 40027663›Full record

ArticlebioRxiv : the preprint server for biology2025

Machine Learning-Enhanced Extraction of Protein Signatures of Renal Cell Carcinoma from Proteomics Data.

Hongyi Liu, Zhuo Ma, T Mamie Lih, Lijun Chen, Yingwei Hu, Yuefan Wang, Zhenyu Sun, Yuanyu Huang, Yuanwei Xu, Hui Zhang

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Hongyi LiuDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.ORCID 0000-0002-9444-3632
Zhuo MaKrieger school of Arts and Sciences, Johns Hopkins University, MD 21218, USA.
T Mamie LihDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
Lijun ChenDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
Yingwei HuDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
Yuefan WangDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.ORCID 0000-0001-5731-6143
Zhenyu SunDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
Yuanyu HuangDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
Yuanwei XuDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
Hui ZhangDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.ORCID 0000-0001-8726-7098

Funding

Proteogenomic Characterization of Tumor Tissues and Preclinical Models with High PrecisionU24CA271079 · NCI · JOHNS HOPKINS UNIVERSITY · PI DANIEL Wanyui CHAN, Hui Zhang · 2022 to 2026
$6.6M
NCI NIH HHS U24 CA271079
6 · The paper itself

Abstract

In this study, we generated label-free data-independent acquisition (DIA)-based liquid chromatography (LC)-mass spectrometry (MS) proteomics data from 261 renal cell carcinomas (RCC) and 195 normal adjacent tissues (NAT). The RCC tumors included 48 non-clear cell renal cell carcinomas (non-ccRCC) and 213 ccRCC. A total of 219,740 peptides and 11,943 protein groups were identified with 9,787 protein groups per sample on average. We adopted a comprehensive approach to select representative samples with different mutation sites, considering histopathological, immune, methylation, and non-negative matrix factorization (NMF)-based subtypes, along with clinical characteristics (gender, grade, and stage) to capture the complexity and diversity of ccRCC tumors. We used machine learning identified 55 protein signatures that distinguish RCC tumors from NATs. Furthermore, 39 protein signatures that differentiate different RCC tumor subtypes were also identified. Our findings offer an extensive perspective of the proteomic landscape in RCC, illuminating specific proteins that serve to distinguish RCC tumors from NATs and among various RCC tumor subtypes.

Indexed as

clear cell renal cell carcinomaClinical Proteomic Tumor Analysis Consortium (CPTAC)data-independent acquisition (DIA)machine learningnon-clear cell renal cell carcinomaproteomicsrenal cell carcinoma

Identifiers

PMID40027663
PMCPMC11870591

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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.