Evidence map›Paper›PMID 37593098›Full record

ArticleFrontiers in oncology2023

Integrated single cell and bulk sequencing analysis identifies tumor reactive CXCR6

Xiaogang Li, Zheng Gao, Jiafeng Chen, Shanru Feng, Xuanming Luo, Yinghong Shi, Zheng Tang, Weiren Liu, Xin Zhang, Ao Huang and 6 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.8field-weighted citation impact, top 13% 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

7 citing papers in PubMed, 8 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

16 authors at 4 institutions in 1 country.

Xiaogang LiDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Zheng GaoDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Jiafeng ChenDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Shanru FengDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Xuanming LuoShanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China.
Yinghong ShiDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Zheng TangDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Weiren LiuDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Xin ZhangDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Ao HuangDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Qiang GaoDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Aiwu KeDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Jian ZhouDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Jia FanDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Xiutao FuDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Zhenbin DingDepartment of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Zhongshan Hospital · CNMinistry of Education of the People's Republic of China · CNFudan University · CNShanghai Xuhui Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Various immune cell types in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) have been identified as important parameters associated with prognosis and responsiveness to immunotherapy. However, how various factors influence immune cell infiltration remains incompletely understood. Hence, we investigated the single cell multi-omics landscape of immune infiltration in HCC, particularly key gene and cell subsets that influence immune infiltration, thus potentially linking the immunotherapy response and immune cell infiltration. Methods: We grouped patients with HCC according to immune cell infiltration scores calculated by single sample gene set enrichment analysis (ssGSEA). Differential expression analysis, functional enrichment, clinical trait association, gene mutation analysis, tumor immune dysfunction and exclusion (TIDE) and prognostic model construction were used to investigate the immune infiltration landscape through multi-omics. Stepwise regression was further used to identify key genes regulating immune infiltration. Single cell analysis was performed to explore expression patterns of candidate genes and investigate associated cellular populations. Correlation analysis, ROC analysis, Immunotherapy cohorts were used to explore and confirm the role of key gene and cellular population in predicting immune infiltration state and immunotherapy response. Immunohistochemistry and multiplexed fluorescence staining were used to further validated our results. Results: Patients with HCC were clustered into high and low immune infiltration groups. Mutations of CTNNB1 and TTN were significantly associated with immune infiltration and altered enrichment of cell populations in the TME. TIDE analysis demonstrated that T cell dysfunction and the T cell exclusion score were elevated in the high and low infiltration groups, respectively. Six risk genes and five risk immune cell types were identified and used to construct risk scores and a nomogram model. CXCR6 and LTA, identified by stepwise regression, were highly associated with immune infiltration. Single cell analysis revealed that LTA was expressed primarily in tumor infiltrating T lymphocytes and partial B lymphocytes, whereas CXCR6 was enriched predominantly in T and NK cells. Notably, CXCR6 Conclusion: We constructed a comprehensive single cell and multi-omics landscape of immune infiltration in HCC, and delineated key genes and cellular populations regulating immune infiltration and immunotherapy response, thus providing insights into the mechanisms of immune infiltration and future therapeutic control.

Indexed as

biomarkerhepatocellular carcinomaimmune infiltrationimmunotherapysingle cell sequencing technologytumor reactive T cells

Identifiers

PMID37593098
PMCPMC10430781
OpenAlexW4385454556

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.