Evidence map›Paper›PMID 35957896›Full record

ArticleFrontiers in oncology2022

Stemness Analysis Uncovers That The Peroxisome Proliferator-Activated Receptor Signaling Pathway Can Mediate Fatty Acid Homeostasis In Sorafenib-Resistant Hepatocellular Carcinoma Cells.

Tingze Feng, Tianzhi Wu, Yanxia Zhang, Lang Zhou, Shanshan Liu, Lin Li, Ming Li, Erqiang Hu, Qianwen Wang, Xiaocong Fu and 6 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.0field-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

8 citing papers in PubMed, 12 citations in OpenAlex.

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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

16 authors at 2 institutions in 1 country.

Tingze FengDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Tianzhi WuDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Yanxia ZhangDepartment of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Lang ZhouDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Shanshan LiuDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Lin LiDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Ming LiDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Erqiang HuDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Qianwen WangDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Xiaocong FuDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Li ZhanDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Zijing XieDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Wenqin XieDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Xianying HuangDivision of Vascular and Interventional Radiology, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Xuan ShangDepartment of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Guangchuang YuDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Southern Medical University · CNNanfang Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) stem cells are regarded as an important part of individualized HCC treatment and sorafenib resistance. However, there is lacking systematic assessment of stem-like indices and associations with a response of sorafenib in HCC. Our study thus aimed to evaluate the status of tumor dedifferentiation for HCC and further identify the regulatory mechanisms under the condition of resistance to sorafenib. Datasets of HCC, including messenger RNAs (mRNAs) expression, somatic mutation, and clinical information were collected. The mRNA expression-based stemness index (mRNAsi), which can represent degrees of dedifferentiation of HCC samples, was calculated to predict drug response of sorafenib therapy and prognosis. Next, unsupervised cluster analysis was conducted to distinguish mRNAsi-based subgroups, and gene/geneset functional enrichment analysis was employed to identify key sorafenib resistance-related pathways. In addition, we analyzed and confirmed the regulation of key genes discovered in this study by combining other omics data. Finally, Luciferase reporter assays were performed to validate their regulation. Our study demonstrated that the stemness index obtained from transcriptomic is a promising biomarker to predict the response of sorafenib therapy and the prognosis in HCC. We revealed the peroxisome proliferator-activated receptor signaling pathway (the PPAR signaling pathway), related to fatty acid biosynthesis, that was a potential sorafenib resistance pathway that had not been reported before. By analyzing the core regulatory genes of the PPAR signaling pathway, we identified four candidate target genes,

Indexed as

hepatocellular carcinomaPPAR signaling pathwayprognosissorafenib resistancestemness index

Identifiers

PMID35957896
PMCPMC9361019
OpenAlexW4287960621

What OpenQuestion holds

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Registered trials

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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.