Evidence map›Paper›PMID 36189217›Full record

ArticleFrontiers in immunology2022

Protective effect of tertiary lymphoid structures against hepatocellular carcinoma: New findings from a genetic perspective.

Weili Jia, Qianyun Yao, Yanfang Wang, Zhenzhen Mao, Tianchen Zhang, Jianhui Li, Ye Nie, Xinjun Lei, Wen Shi, Wenjie Song

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 6% 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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

  1. Pooled it
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  13. Tertiary lymphoid structures and their therapeutic implications in cancer.Cellular oncology (Dordrecht, Netherlands) · 2024
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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

10 authors at 4 institutions in 1 country.

Weili JiaXi'an Medical University, Xi'an, China.
Qianyun YaoXi'an Medical University, Xi'an, China.
Yanfang WangXi'an Medical University, Xi'an, China.
Zhenzhen MaoXi'an Medical University, Xi'an, China.
Tianchen ZhangXi'an Medical University, Xi'an, China.
Jianhui LiDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Ye NieDepartment of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Xinjun LeiXi'an Medical University, Xi'an, China.
Wen ShiXi'an Medical University, Xi'an, China.
Wenjie SongDepartment of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Xi'an Medical University · CNXijing Hospital · CNAir Force Medical University · CNAnhui Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Tertiary lymphoid structures (TLS) have an effect on hepatocellular carcinoma (HCC), but the underlying mechanism remains to be elucidated. Methods: Intratumoral TLS (iTLS) was classified in the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort using pathological sections from the Cancer Digital Slide Archive. Univariate and multivariate Cox regression analyses were performed to validate the effect of iTLS on overall survival (OS), relapse-free survival (RFS), and disease-free survival (DFS). The genes differentially expressed between the iTLS-negative and iTLS-positive groups were analyzed in combination with sequencing data. Gene set enrichment analysis (GSEA) was used to explore the signaling pathways affected by these differentially expressed genes. The random forest algorithm was used to identify genes with the highest correlation with the iTLS in the training set. Multivariate logistic regression was used to build a model to predict iTLS in tissue samples. Spearman's correlation was used to analyze the relationship between TLS-associated chemokines and signature genes, and CIBERSORT was used to calculate immune infiltration scores. Copy number variation and its relationship with immune cell infiltration and signature genes were assessed using the gene set cancer analysis (GSCA). The Correlation R package was used for gene ontology (GO), disease ontology (DO), and gene mutation analyses. The GSCA was used for drug sensitivity analysis. LASSO regression was used to build prognostic models, and external data were used to validate the models. Results: There were 218 positive and 146 negative samples for iTLS. iTLS was significantly associated with better RFS and DFS according to Cox regression analysis. Twenty signature genes that were highly associated with iTLS positivity were identified. GO and mutation analyses revealed that the signature genes were associated with immunity. Most signature genes were sensitive to immune checkpoint inhibitors. Risk scores calculated using a characteristic gene-based prognostic model were found to be an independent prognostic factor for OS. Conclusions: The improvement of RFS in HCC by iTLS was not limited to the early period as previously reported. iTLS improved DFS in patients. Characteristic genes are closely related to the formation of iTLS and TLS chemokines in HCC. These genes are closely related to immunity in terms of cellular infiltration, biological functions, and signaling pathways. Most are sensitive to immune checkpoint inhibitors, and their expression levels can affect prognosis.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsTertiary Lymphoid StructuresBiomarkers, TumorDNA Copy Number VariationsGene Expression ProfilingGene Expression Regulation, NeoplasticHumansImmune Checkpoint InhibitorsKaplan-Meier EstimateNeoplasm Recurrence, LocalBiomarkers, TumorImmune Checkpoint Inhibitorscancer prognosishepatocellular carcinoma (HCC)immune microenvironment (IME)immunotherapytertiary lymphoid structures (TLS)

Identifiers

PMID36189217
PMCPMC9515394
OpenAlexW4295993478

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.