Evidence map›Paper›PMID 36527145›Full record

ArticleGenome medicine2022

Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer.

Chen Yang, Senquan Zhang, Zhuoan Cheng, Zhicheng Liu, Linmeng Zhang, Kai Jiang, Haigang Geng, Ruolan Qian, Jun Wang, Xiaowen Huang and 7 more

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
2.9field-weighted citation impact, top 7% 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

37 citing papers in PubMed, 33 citations in OpenAlex.

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  15. [Study on the value of abnormal prothrombin in the diagnosis of HBV-related hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025
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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

17 authors at 2 institutions in 1 country.

Chen Yang *Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Senquan Zhang *Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhuoan Cheng *Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhicheng Liu *Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Linmeng Zhang *State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Kai JiangRenji Biobank, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Haigang GengDepartment of Gastrointestinal Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China.
Ruolan QianState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jun WangState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaowen HuangKey Laboratory of Gastroenterology and Hepatology, Division of Gastroenterology and Hepatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Mo ChenDepartment of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhe LiDepartment of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Wenxin QinState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qiang XiaDepartment of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. xiaqiang@shsmu.edu.cn.
Xiaonan KangRenji Biobank, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. xnkang7@163.com.
Cun WangDepartment of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. cwang@shsci.org.
Hualian HangDepartment of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. hanghualian@shsmu.edu.cn.ORCID 0000-0002-3000-7647
Shanghai Jiao Tong University · CNTongji Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNumerous studies have used multi-region sampling approaches to characterize intra-tumor heterogeneity (ITH) in hepatocellular carcinoma (HCC). However, conventional multi-region sampling strategies do not preserve the spatial details of samples, and thus, the potential influences of spatial distribution on patient-wise ITH (represents the overall heterogeneity level of the tumor in a given patient) have long been overlooked. Furthermore, gene-wise transcriptional ITH (represents the expression pattern of genes across different intra-tumor regions) in HCC is also under-explored, highlighting the need for a comprehensive investigation.

methodsTo address the problem of spatial information loss, we propose a simple and easy-to-implement strategy called spatial localization sampling (SLS). We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. Normalized diversity score and integrated heterogeneity score (IHS) were then developed to measure patient-wise and gene-wise ITH, respectively.

resultsA significant correlation between spatial and molecular heterogeneity was uncovered, implying that spatial distribution of sampling sites did influence ITH estimation in HCC. We demonstrated that the normalized diversity score had the ability to overcome sampling location bias and provide a more accurate estimation of patient-wise ITH. According to this metric, HCC tumors could be divided into two classes (low-ITH and high-ITH tumors) with significant differences in multiple biological properties. Through IHS analysis, we revealed a highly heterogenous immune microenvironment in HCC and identified some low-ITH checkpoint genes with immunotherapeutic potential. We also constructed a low-heterogeneity risk stratification (LHRS) signature based on the IHS results which could accurately predict the survival outcome of patients with HCC on a single tumor biopsy sample.

conclusionsThis study provides new insights into the complex phenotypes of HCC and may serve as a guide for future studies in this field.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsHigh-Throughput Nucleotide SequencingHumansRisk AssessmentTumor MicroenvironmentHepatocellular carcinomaImmunotherapyIntra-tumor heterogeneityPrognostication

Identifiers

PMID36527145
PMCPMC9758830
OpenAlexW4311896038

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LicenceCC BY
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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.