Evidence map›Paper›PMID 36246969›Full record

ArticleBioMed research international2022

Development and Optimization of a Prognostic Model Associated with Stemness Genes in Hepatocellular Carcinoma.

Kefen Zhang, Kaisheng Xie, Xin Huo, Lianlian Liu, Jilin Liu, Chao Zhang, Jun Wang

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 35% 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. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Kefen ZhangGuangxi University of Science and Technology, Liuzhou 545006, China.
Kaisheng XieDepartment of Pathology, Second Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou 545006, China.
Xin HuoDepartment of Oncology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou 545006, China.
Lianlian LiuCollege of Information Science and Engineering, Shandong Agricultural University, Tai'an 271000, China.
Jilin LiuCollege of Information Science and Engineering, Shandong Agricultural University, Tai'an 271000, China.
Chao ZhangCollege of Information Science and Engineering, Shandong Agricultural University, Tai'an 271000, China.ORCID https://orcid.org/0000-0003-4481-7613
Jun WangDepartment of Oncology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou 545006, China.ORCID https://orcid.org/0000-0001-8411-9196
Shandong Agricultural University · CNGuangxi University of Science and Technology · CNLiuzhou General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, which is associated with a variety of risk factors. Cancer stem cells are self-renewal cells, which can promote the occurrence and metastasis of tumors and enhance the drug resistance of tumor treatment. This study aimed to develop a stemness score model to assess the prognosis of hepatocellular carcinoma (HCC) patients for the optimization of treatment. The single-cell sequencing data GSE149614 was downloaded from the GEO database. Then, we compared the gene expression of hepatic stem cells and other hepatocytes in tumor samples to screen differentially expressed genes related to stemness. R package "clusterProfiler" was used to explore the potential function of stemness-related genes. We then constructed a prognostic model using LASSO regression analysis based on the TCGA and GSE14520 cohorts. The associations of stemness score with clinical features, drug sensitivity, gene mutation, and tumor immune microenvironment were further explored. R package "rms" was used to construct the nomogram model. A total of 18 stemness-related genes were enrolled to construct the prognosis model. Kaplan-Meier analysis proved the good performance of the stemness score model at predicting overall survival (OS) of HCC patients. The stemness score was closely associated with clinical features, drug sensitivity, and tumor immune microenvironment of HCC. The infiltration level of CD8

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsBiomarkers, TumorCD8-Positive T-LymphocytesHumansPrognosisTumor MicroenvironmentBiomarkers, Tumor

Identifiers

PMID36246969
PMCPMC9556181
OpenAlexW4302283584

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.