Evidence map›Paper›PMID 33820866›Full record

ArticleAging2021

Prognostic analysis of tumor mutation burden and immune infiltration in hepatocellular carcinoma based on TCGA data.

Shaoqing Liu, Qianjie Tang, Jianwen Huang, Meixiao Zhan, Wei Zhao, Xiangyu Yang, Yong Li, Lige Qiu, Fujun Zhang, Ligong Lu and 1 more

Open access · greenAbstract read
In one paragraph

Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
3.6field-weighted citation impact, top 5% 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

21 citing papers in PubMed, 44 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

11 authors at 4 institutions in 1 country.

Shaoqing LiuDepartment of Minimally Invasive and Interventional Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Qianjie TangDepartment of Pharmacy, Guangdong Women and Children Hospital, Guangzhou 511400, China.
Jianwen HuangZhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital Affiliated with Jinan University), Jinan University, Zhuhai 519000, China.
Meixiao ZhanZhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital Affiliated with Jinan University), Jinan University, Zhuhai 519000, China.
Wei ZhaoZhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital Affiliated with Jinan University), Jinan University, Zhuhai 519000, China.
Xiangyu YangZhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital Affiliated with Jinan University), Jinan University, Zhuhai 519000, China.
Yong LiZhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital Affiliated with Jinan University), Jinan University, Zhuhai 519000, China.
Lige QiuZhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital Affiliated with Jinan University), Jinan University, Zhuhai 519000, China.
Fujun ZhangDepartment of Minimally Invasive and Interventional Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Ligong LuZhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital Affiliated with Jinan University), Jinan University, Zhuhai 519000, China.
Xu HeZhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital Affiliated with Jinan University), Jinan University, Zhuhai 519000, China.
Zhuhai People's Hospital · CNJinan University · CNGuangdong Province Women and Children Hospital · CNSun Yat-sen University Cancer Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to explore the prognosis of tumor mutation burden (TMB) and the relationship with tumor infiltrating immune cells in hepatocellular carcinoma (HCC), we downloaded somatic mutation data and transcriptome profiles of 376 HCC patients from The Cancer Genome Atlas (TCGA) cohort. We divided the samples into high-TMB and low-TMB groups. A higher TMB level indicated improved overall survival (OS) and was associated with early pathological stages. One hundred and nine differentially expressed genes (DEGs) were identified in HCC. Moreover, based on four hub TMB-related signatures, we constructed a TMB Prognostic model (TMBPM) that possessed good predictive value with area under curve (AUC) of 0.701. HCC patients with higher TMBPM scores showed worse OS outcomes (p < 0.0001). Moreover, DCs subsets not only revealed higher infiltrating abundance in the high-TMB group, but also correlated with worse OS and hazard risk for high-TMB patients in HCC. Meanwhile, CD8+ T cells and B cells were associated with improved survival outcomes. In sum, high TMB indicates good prognosis for HCC and promotes HCC immune infiltration. Hence, DCs and the four hub TMB-related signatures can be used for predicting the prognosis in HCC as supplements to TMB.

Indexed as

AgedBiomarkers, TumorCarcinoma, HepatocellularCohort StudiesDatabases, GeneticDatasets as TopicDisease-Free SurvivalDNA Mutational AnalysisFemaleGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateLiverLiver NeoplasmsMaleMiddle AgedBiomarkers, Tumorhepatocellular carcinomaimmune infiltrationThe Cancer Genome Atlastumor mutation burden

Identifiers

PMID33820866
PMCPMC8109113
OpenAlexW3145307137

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.