Evidence map›Paper›PMID 34594324›Full record

ArticleFrontiers in immunology2021

Novel Immune Infiltrating Cell Signature Based on Cell Pair Algorithm Is a Prognostic Marker in Cancer.

Hao Zhang, Zeyu Wang, Ziyu Dai, Wantao Wu, Hui Cao, Shuyu Li, Nan Zhang, Quan Cheng

Abstract read
In one paragraph

Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
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

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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

31 citing papers in PubMed.

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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

8 authors.

Hao ZhangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Zeyu WangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Ziyu DaiDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Wantao WuDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, China.
Hui CaoDepartment of Psychiatry, The Second People's Hospital of Hunan Province, The Hospital of Hunan University of Chinese Medicine, Changsha, China.
Shuyu LiDepartment of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China.
Nan ZhangOne-third Lab, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Quan ChengDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor-infiltrating immune cells (TIICs) have become an important source of markers for predicting the clinical outcomes of cancer patients. However, measurements of cellular heterogeneity vary due to the frequently updated reference genomes and gene annotations. In this study, we systematically collected and evaluated the infiltration pattern of 65 immune cells. We constructed the Immune Cell Pair (ICP) score based on the cell pair algorithm in 3,715 samples and across 12 independent cancer types, among which, the ICP score from six cancer types was further validated in 2,228 GEO samples. An extensive tumorigenic and immunogenomic analysis was subsequently conducted. As a result, the ICP score showed a robust reliability and efficacy in predicting the survival of patients with gliomas, in pan-cancer samples, and six independent cancer types. Notably, the ICP score was correlated with the genomic alteration features in gliomas. Moreover, the ICP score exhibited a remarkable association with multiple immunomodulators that could potentially mediate immune escape. Finally, the ICP score predicted immunotherapeutic responses with a high sensitivity, allowing a useful tool for predicting the overall survival and guiding immunotherapy for cancer patients.

Indexed as

AlgorithmsGene Expression ProfilingImmunogeneticsTranscriptomeTumor MicroenvironmentBiomarkers, TumorBrain NeoplasmsDatabases, GeneticGliomaHumansImmunotherapyPredictive Value of TestsPrognosisTumor EscapeBiomarkers, Tumorcell pair algorithmglioma microenvironmentimmune cellimmunotherapyprognostic model

Identifiers

PMID34594324
PMCPMC8476752

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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.