Evidence map›Paper›PMID 36645555›Full record

ArticleBiochemical genetics2023

Transcriptomics Based Network Analyses and Molecular Docking Highlighted Potentially Therapeutic Biomarkers for Colon Cancer.

Yun Wu, Xiaoping Dong, Jia Hu, Lingxiang Wang, Rongfang Xu, Yongjun Wang, Yong Zeng

Abstract read
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In one paragraph

Article in Biochemical genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  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

7 authors at 3 institutions in 1 country.

Yun Wu *National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410011, Hunan, China.
Xiaoping Dong *National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410011, Hunan, China.
Jia HuDepartment of Gastroenterology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China.
Lingxiang WangNational & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410011, Hunan, China.
Rongfang XuNational & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410011, Hunan, China.
Yongjun WangDepartment of Gastroenterology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China. yongjunwang@csu.edu.cn.
Yong ZengNational & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410011, Hunan, China. yongz@hunnu.edu.cn.
Hunan Normal University · CNSecond Xiangya Hospital of Central South University · CNCentral South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, machine learning-based multiple bioinformatics analysis was carried out for the purpose of the deep and efficient mining of high-throughput transcriptomics data from the TCGA database. Compared with normal colon tissue, 2469 genes were significantly differentially expressed in colon cancer tissue. Gene functional annotation and pathway analysis suggested that most DEGs were functionally related to the cell cycle and metabolism. Weighted gene co-expression network analysis revealed a significant module and the enriched genes that were closely related to fatty acid degradation and metabolism. Based on colon cancer progression, the trend analysis highlighted that several gene sets were significantly correlated with disease development. At the same time, the most specific genes were functionally related to cancer cell features such as the high performance of DNA replication and cell division. Moreover, survival analysis and target drug prediction were performed to prioritize reliable biomarkers and potential drugs. In consideration of a combination of different evidence, four genes (ACOX1, CPT2, CDC25C and PKMYT1) were suggested as novel biomarkers in colon cancer. The potential biomarkers and target drugs identified in our study may provide new ideas for colonic-related prevention, diagnosis, and treatment; therefore, our results have high clinical value for colon cancer.

Indexed as

Colonic NeoplasmsTranscriptomeBiomarkersBiomarkers, TumorComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMembrane ProteinsMolecular Docking SimulationProtein Serine-Threonine KinasesProtein-Tyrosine KinasesBiomarkersBiomarkers, TumorMembrane ProteinsPKMYT1 protein, humanProtein Serine-Threonine KinasesProtein-Tyrosine KinasesBioinformaticsBiomarkersColon cancerMachine learningMulti-omicsTranscriptomics

Identifiers

PMID36645555
OpenAlexW4316469872

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.