Evidence map›Paper›PMID 31645679›Full record

ArticleCancer gene therapy2020

Identification of genes of four malignant tumors and a novel prediction model development based on PPI data and support vector machines.

Ming Li, Ping Wang, Ning Zhang, Lu Guo, Yuan-Ming Feng

Abstract read
PubMed Publisher
In one paragraph

Article in Cancer gene therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. Article
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

5 authors at 2 institutions in 1 country.

Ming Li *Department of Biomedical Engineering, Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin, PR China.
Ping WangDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, Tianjin, PR China. wangping@tjmuch.com.
Ning Zhang *Department of Biomedical Engineering, Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin, PR China. zhni@tju.edu.cn.ORCID http://orcid.org/0000-0002-1559-111X
Lu GuoDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, Tianjin, PR China.
Yuan-Ming FengDepartment of Biomedical Engineering, Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin, PR China.
Tianjin University · CNTianjin Medical University Cancer Institute and Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Triple-negative breast cancer (TNBC), colon adenocarcinoma (COAD), ovarian cancer (OV), and glioblastoma multiforme (GBM) are common malignant tumors, in which significant challenges are still faced in early diagnosis, treatment, and prognosis. Therefore, further identification of genes related to those malignant tumors is of great significance for the improvement of management of the diseases. The database of the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) repository was used as the data source of gene expression profiles in this study. Malignant tumors genes were selected using a feature selection algorithm of maximal relevance and minimal redundancy (mRMR) and the protein-protein interaction (PPI) network. And finally selected 20 genes as potential related genes. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the potential related genes, and different tumor-specific genes and similarities and differences between network modules and pathways were analyzed. Further, using the potential cancer-related genes found above in this study as features, a support vector machine (SVM) model was developed to predict high-risk malignant tumors. As a result, the prediction accuracy reached more than 85%, indicating that such a model can effectively predict the four types of malignant tumors. It is demonstrated that such genes found above in this study indeed play important roles in the differentiation of the four types of malignant tumors, providing basis for future experimental biological validation and shedding some light on the understanding of new molecular mechanisms related to the four types of tumors.

Indexed as

FemaleGene Regulatory NetworksHumansMaleNeoplasmsPrognosisProtein Interaction MapsSupport Vector Machine

Identifiers

PMID31645679
OpenAlexW2981978164

What OpenQuestion holds

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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.