Evidence map›Paper›PMID 34350180›Full record

ArticleFrontiers in cell and developmental biology2021

RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma.

Benchen Rao, Jianhao Li, Tong Ren, Jing Yang, Guizhen Zhang, Liwen Liu, Haiyu Wang, Maoxin Huang, Zhigang Ren, Zujiang Yu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

16 citing papers in PubMed, 22 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

10 authors at 2 institutions in 1 country.

Benchen RaoDepartment of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jianhao LiDepartment of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Tong RenDepartment of Breast Surgery, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China.
Jing YangDepartment of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Guizhen ZhangDepartment of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Liwen LiuDepartment of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Haiyu WangDepartment of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Maoxin HuangDepartment of Dermatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Zhigang RenDepartment of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Zujiang YuDepartment of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
First Affiliated Hospital of Zhengzhou University · CNZhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.

methodsThe differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.

resultsA total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (

conclusionOur study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

Indexed as

hepatocellular carcinomaimmune infiltrationprognostic biomarkerribosomal protein L19weighted gene co-expression network analysis

Identifiers

PMID34350180
PMCPMC8327752
OpenAlexW3185632242

What OpenQuestion holds

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LicenceCC BY
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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.