Evidence map›Paper›PMID 35345553›Full record

ArticleJournal of hepatocellular carcinoma2022

LRP1B is a Potential Biomarker for Tumor Immunogenicity and Prognosis of HCC Patients Receiving ICI Treatment.

Yang Cheng, Rui Tang, Xiangzhao Li, Biao Wang, Yanling Cheng, Shuzhe Xiao, Penghui Sun, Wenxuan Yu, Cheng Li, Xinsheng Lin and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of hepatocellular carcinoma, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.8field-weighted citation impact, top 14% 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

12 citing papers in PubMed, 13 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 3 institutions in 1 country.

Yang Cheng *Digestive Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, People's Republic of China.
Rui Tang *Digestive Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, People's Republic of China.
Xiangzhao LiDepartment of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Biao WangDepartment of Hepatobiliary Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Yanling ChengDigestive Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, People's Republic of China.
Shuzhe XiaoDigestive Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, People's Republic of China.
Penghui SunNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Wenxuan YuLiver Tumor Center, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Cheng LiLiver Tumor Center, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Xinsheng LinLiver Tumor Center, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Yun Zhu *Liver Tumor Center, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Nanfang Hospital · CNGuangzhou Medical University · CNSouthern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: New predictors of the efficacy of hepatocellular carcinoma (HCC) immunotherapy are needed. The ability of a single gene mutation to predict the therapeutic effect of immune checkpoint inhibitors (ICI) in HCC remains unknown. Methods: The most frequently mutated genes in HCC were analyzed using the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Mutant genes that correlated with the tumor mutational burden (TMB) and prognosis were obtained. The mutation pattern and immunological function of one of the most frequently mutated genes, LRP1B, were determined. A pan-tumor analysis of LRP1B expression, association with cancer prognosis, and immunological role was also explored. A retrospective clinical study was conducted using 102 HCC patients who received ICI treatment to further verify whether gene mutations can predict the effectiveness of immunotherapy and prognosis of HCC. Results: LRP1B is among the most frequently mutated genes in HCC cohorts in TCGA and ICGC datasets. TCGA data showed that the LRP1B mutation activated immune signaling pathways and promoted mast cell activation. Patients with LRP1B mutations had significantly higher TMB than those with wild-type LRP1B. LRP1B expression correlated with the cancer-immunity cycle and immune cell infiltration. High LRP1B expression was also associated with poor survival among HCC patients. Results from the clinical study showed that HCC patients in the LRP1B mutation group had a poor response to ICI and worse prognosis than the wild-type group. The LRP1B mutation group had significantly higher TMB and mast cell infiltration in tumor tissues. Conclusion: This study is the first to report that a single gene LRP1B mutation is associated with a poor clinical response to ICI treatment and negative outcomes in HCC patients. HighLRP1B expression correlated with tumor immunity and HCC prognosis.

Indexed as

HCCICI treatment responseLRP1B mutationprognosisTMB

Identifiers

PMID35345553
PMCPMC8957351
OpenAlexW4220882560

What OpenQuestion holds

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LicenceCC BY-NC
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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.