Evidence map›Paper›PMID 35421268›Full record

ArticleJournal of clinical laboratory analysis2022

Identification and construction of a 13-gene risk model for prognosis prediction in hepatocellular carcinoma patients.

Daming Cheng, Libing Wang, Fengzhi Qu, Jingkun Yu, Zhaoyuan Tang, Xiaogang Liu

Abstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  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

6 authors.

Daming ChengDepartment of Hepatobiliary Surgery, Tangshan Gongren Hospital, Tangshan City, China.
Libing WangDepartment of Hepatobiliary Surgery, Tangshan Gongren Hospital, Tangshan City, China.
Fengzhi QuDepartment of Hepatobiliary Surgery, Tangshan Gongren Hospital, Tangshan City, China.
Jingkun YuDepartment of Hepatobiliary Surgery, Tangshan Gongren Hospital, Tangshan City, China.
Zhaoyuan TangDepartment of Hepatobiliary Surgery, Tangshan Gongren Hospital, Tangshan City, China.
Xiaogang LiuDepartment of Hepatobiliary Surgery, Tangshan Gongren Hospital, Tangshan City, China.ORCID https://orcid.org/0000-0001-5647-6946

Funding

Hebei Province 2021 Medical Science Research Project 20211334
6 · The paper itself

Abstract

We attempted to screen out the feature genes associated with the prognosis of hepatocellular carcinoma (HCC) patients through bioinformatics methods, to generate a risk model to predict the survival rate of patients. Gene expression information of HCC was accessed from GEO database, and differentially expressed genes (DEGs) were obtained through the joint analysis of multi-chip. Functional and pathway enrichment analyses of DEGs indicated that the enrichment was mainly displayed in biological processes such as nuclear division. Based on TCGA-LIHC data set, univariate, LASSO, and multivariate Cox regression analyses were conducted on the DEGs. Then, 13 feature genes were screened for the risk model. Also, the hub genes were examined in our collected clinical samples and GEPIA database. The performance of the risk model was validated by Kaplan-Meier survival analysis and receiver operation characteristic (ROC) curves. While its universality was verified in GSE76427 and ICGC (LIRI-JP) validation cohorts. Besides, through combining patients' clinical features (age, gender, T staging, and stage) and risk scores, univariate and multivariate Cox regression analyses revealed that the risk score was an effective independent prognostic factor. Finally, a nomogram was implemented for 3-year and 5-year overall survival prediction of patients. Our findings aid precision prediction for prognosis of HCC patients.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsBiomarkers, TumorGene Expression ProfilingHumansPrognosisRisk FactorsBiomarkers, Tumorbioinformatics methodsfeature geneshepatocellular carcinomaprognosisrisk model

Identifiers

PMID35421268
PMCPMC9102505

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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.