Evidence map›Paper›PMID 35487057›Full record

ArticleEBioMedicine2022

Individualized pathway activity algorithm identifies oncogenic pathways in pan-cancer analysis.

Xin Ke, Hao Wu, Yi-Xiao Chen, Yan Guo, Shi Yao, Ming-Rui Guo, Yuan-Yuan Duan, Nai-Ning Wang, Wei Shi, Chen Wang and 4 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.9field-weighted citation impact, top 28% of its field
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

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
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  3. Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024
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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

14 authors at 3 institutions in 1 country.

Xin KeKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China.
Hao WuKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China.
Yi-Xiao ChenNational and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, PR China.
Yan GuoKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China.
Shi YaoNational and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, PR China.
Ming-Rui GuoKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China.
Yuan-Yuan DuanKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China.
Nai-Ning WangKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China.
Wei ShiKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China.
Chen WangKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China.
Shan-Shan DongKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China.
Huafeng KangDepartment of Oncology, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, PR China.
Zhijun DaiDepartment of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, PR China.
Tie-Lin YangKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, PR China. Electronic address: yangtielin@xjtu.edu.cn.
Xi'an Jiaotong University · CNSecond Affiliated Hospital of Xi'an Jiaotong University · CNZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccumulative evidences have shown that dysregulation of biological pathways contributed to the initiation and progression of malignant tumours. Several methods for pathway activity measurement have been proposed, but they are restricted to making comparisons between groups or sensitive to experimental batch effects.

methodsWe introduced a novel method for individualized pathway activity measurement (IPAM) that is based on the ranking of gene expression levels in individual sample. Taking advantage of IPAM, we calculated the pathway activity of 318 pathways from KEGG database in the 10528 tumour/normal samples of 33 cancer types from TCGA to identify characteristic dysregulated pathways among different cancer types.

findingsIPAM precisely quantified the level of activity of each pathway in pan-cancer analysis and exhibited better performance in cancer classification and prognosis prediction over five widely used tools. The average ROC-AUC of cancer diagnostic model using tumour-educated platelets (TEPs) reached 92.84%, suggesting the potential of our algorithm in early diagnosis of cancer. We identified several pathways significantly deregulated and associated with patient survival in a large fraction of cancer types, such as tyrosine metabolism, fatty acid degradation, cell cycle, p53 signalling pathway and DNA replication. We also confirmed the dominant role of metabolic pathways in cancer pathway dysregulation and identified the driving factors of specific pathway dysregulation, such as PPARA for branched-chain amino acid metabolism and NR1I2, NR1I3 for fatty acid metabolism.

interpretationOur study will provide novel clues for understanding the pathological mechanisms of cancer, ultimately paving the way for personalized medicine of cancer.

fundingA full list of funding can be found in the Acknowledgements section.

Indexed as

NeoplasmsOncogenesAlgorithmsCarcinogenesisFatty AcidsGene Expression ProfilingHumansFatty AcidsKEGG pathwaysOncogenic pathwaysPan-cancer analysisPathway activity algorithm

Identifiers

PMID35487057
PMCPMC9117264
OpenAlexW4224452069

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.