Evidence map›Paper›PMID 36785674›Full record

ArticleComputational and mathematical methods in medicine2023

Identification of a Five Immune Term Signature for Prognosis and Therapy Options (Immunotherapy versus Targeted Therapy) for Patients with Hepatocellular Carcinoma.

Xiaoyun Bin, Zongjiang Luo, Jianchu Wang, Sufang Zhou

Open access · hybridAbstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 22% 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

6 citing papers in PubMed, 4 citations in OpenAlex.

  1. DamageZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026
    Article
  2. Article
  3. ERCC1/NGFR affects prognosis in basal-like breast cancer.European journal of medical research · 2025
    Article
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  6. Review
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.

Xiaoyun BinDepartment of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi 530021, China.
Zongjiang LuoDepartment of Hepatobiliary Surgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi 533000, China.
Jianchu WangDepartment of Hepatobiliary Surgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi 533000, China.ORCID https://orcid.org/0000-0002-2421-6450
Sufang ZhouDepartment of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi 530021, China.ORCID https://orcid.org/0000-0001-5597-1726
Affiliated Hospital of Youjiang Medical University for Nationalities · CNGuangxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Immune microenvironment implicated in liver cancer development. Nevertheless, previous studies have not fully investigated the immune microenvironment in liver cancer. Methods: The open-access data used for analysis were obtained from The Cancer Genome Atlas (TCGA-LIHC) and the International Cancer Genome Consortium databases (ICGC-JP and ICGC-FR). R program was employed to analyze all the data statistically. Results: First, the TCGA-LIHC, ICGC-FR, and ICGC-JP cohorts were selected for our analysis, which were merged into a combined cohort. Then, we quantified 53 immune terms in this combined cohort with large populations using the ssGSEA algorithm. Next, a prognostic approach was established based on five immune principles (CORE.SERUM.RESPONSE.UP, angiogenesis, CD8.T.cells, Th2.cells, and B.cells) was established, which showed great prognostic prediction efficiency. Clinical correlation analysis demonstrated that high-risk patients could reveal higher progressive clinical features. Next, to examine the inherent biological variations in high- and low-risk patients, pathway enrichment tests were conducted. DNA repair, E2F targets, G2M checkpoints, HEDGEHOG signaling, mTORC1 signaling, and MYC target were positively correlated with the risk score. Examination of genomic instability revealed that high-risk patients may exhibit a higher tumor mutation burden score. Meanwhile, the risk score showed a strong positive correlation with the tumor stemness index. In addition, the Tumor Immune Dysfunction and Exclusion outcome indicated that high-risk patients could be higher responsive to immunotherapy, whereas low-risk patients may be higher responsive to Erlotinib. Finally, six characteristic genes DEPDC1, DEPDC1B, NGFR, CALCRL, PRR11, and TRIP13 were identified for risk group prediction. Conclusions: In summary, our study identified a signature as a useful tool to indicate prognosis and therapy options for liver cancer patients.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsATPases Associated with Diverse Cellular ActivitiesCell Cycle ProteinsGTPase-Activating ProteinsHedgehog ProteinsHumansImmunotherapyNeoplasm ProteinsPrognosisTumor MicroenvironmentATPases Associated with Diverse Cellular ActivitiesCell Cycle ProteinsDEPDC1B protein, humanDEPDC1 protein, humanGTPase-Activating ProteinsHedgehog ProteinsNeoplasm ProteinsTRIP13 protein, human

Identifiers

PMID36785674
PMCPMC9918845
OpenAlexW4318993304

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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.