Evidence map›Paper›PMID 35681134›Full record

ArticleBMC cancer2022

Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma.

Jingrui Wang, Sunbin Ling, Jie Ni, Yafeng Wan

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

5 citing papers in PubMed, 3 citations in OpenAlex.

  1. Unraveling the Causal Links Between Immune Traits and Hepatocellular Carcinoma: Insights From a Bi-Directional Mendelian Randomization Study.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2025
    Article
  2. Article
  3. Review
  4. Article
  5. Article
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

4 authors at 2 institutions in 1 country.

Jingrui WangDepartment of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No.261, Huansha Road, Zhejiang, Hangzhou, China.
Sunbin LingDepartment of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No.261, Huansha Road, Zhejiang, Hangzhou, China.
Jie NiDepartment of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No.261, Huansha Road, Zhejiang, Hangzhou, China.
Yafeng WanDepartment of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No.261, Huansha Road, Zhejiang, Hangzhou, China. wanyafeng@hotmail.com.
Hangzhou First People's Hospital · CNZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNumerous studies have revealed that gamma delta (γδ) T cell infiltration plays a crucial regulatory role in hepatocellular carcinoma (HCC) development. Nonetheless, a comprehensive analysis of γδ T cell infiltration in prognosis evaluation and therapeutic prediction remains unclear.

methodsMulti-omic data on HCC patients were obtained from public databases. The CIBERSORT algorithm was applied to decipher the tumor immune microenvironment (TIME) of HCC. Weighted gene co-expression network analysis (WGCNA) was performed to determine significant modules with γδ T cell-specific genes. Kaplan-Meier survival curves and receiver operating characteristic analyses were used to validate prognostic capability. Additionally, the potential role of RFESD inhibition by si-RFESD in vitro was investigated using EdU and CCK-8 assays.

resultsA total of 16,421 genes from 746 HCC samples (616 cancer and 130 normal) were identified based on three distinct cohorts. Using WGCNA, candidate modules (brown) with 1755 significant corresponding genes were extracted as γδ T cell-specific genes. Next, a novel risk signature consisting of 11 hub genes was constructed using multiple bioinformatic analyses, which presented great prognosis prediction reliability. The risk score exhibited a significant correlation with ICI and chemotherapeutic targets. HCC samples with different risks experienced diverse signalling pathway activities. The possible interaction of risk score with tumor mutation burden (TMB) was further analyzed. Subsequently, the potential functions of the RFESD gene were explored in HCC, and knockdown of RFESD inhibited cell proliferation in HCC cells. Finally, a robust prognostic risk-clinical nomogram was developed and validated to quantify clinical outcomes.

conclusionsCollectively, comprehensive analyses focusing on γδ T cell patterns will provide insights into prognosis prediction, the mechanisms of immune infiltration, and advanced therapy strategies in HCC.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsGene Expression ProfilingHumansPrognosisReproducibility of ResultsT-LymphocytesTumor MicroenvironmentHepatocellular carcinomaPrognosis preditionRFESDTumor immune microenvironmentTumor mutation burdenγδ T cells

Identifiers

PMID35681134
PMCPMC9185956
OpenAlexW4281912230

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.