Evidence map›Paper›PMID 34249407›Full record

ReviewAmerican journal of cancer research2021

Recent progress on MHC-I epitope prediction in tumor immunotherapy.

Xiangyi Wang, Zhaojin Yu, Wensi Liu, Haichao Tang, Dongxu Yi, Minjie Wei

Open access · greenAbstract readReview
In one paragraph

Review in American journal of cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 36% 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

6 citing papers in PubMed, 7 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

6 authors at 2 institutions in 1 country.

Xiangyi WangDepartment of Pharmacology, School of Pharmacy, China Medical University No. 77 Puhe Road, Shenyang North New District, Shenyang, Liaoning, P. R. China.
Zhaojin YuDepartment of Pharmacology, School of Pharmacy, China Medical University No. 77 Puhe Road, Shenyang North New District, Shenyang, Liaoning, P. R. China.
Wensi LiuDepartment of Pharmacology, School of Pharmacy, China Medical University No. 77 Puhe Road, Shenyang North New District, Shenyang, Liaoning, P. R. China.
Haichao TangDepartment of Pharmacology, School of Pharmacy, China Medical University No. 77 Puhe Road, Shenyang North New District, Shenyang, Liaoning, P. R. China.
Dongxu YiThe Affiliated Reproductive Hospital of China Medical University No. 10 Puhe Street, Huanggu District Shenyang, Liaoning, P. R. China.
Minjie WeiDepartment of Pharmacology, School of Pharmacy, China Medical University No. 77 Puhe Road, Shenyang North New District, Shenyang, Liaoning, P. R. China.
Shenyang Medical College · CNChina Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor immunotherapy has now become one of the most potential therapy for those intractable cancer diseases. The antigens on the cancer cell surfaces are the keys for the immune system to recognize and eliminate them. As reported, the immunogenicity of the tumor antigens could be determined by the binding between the key epitope peptides and MHC molecules. In recent years, the approaches to anticipate the peptides from the candidate epitopes have gradually changed into more efficient methods. Including the improved conventional methods, more diverse methods were coming into view. Here we review the anticipated methods of the tumor associated epitopes that specifically bind with major histocompatibility complex (MHC) class I molecules, and the recent advances and applications of those epitope prediction methods.

Indexed as

CTL epitopesepitope predictionMHC class I moleculeprediction and identificationtumor immunotherapy

Identifiers

PMID34249407
PMCPMC8263640
OpenAlexW3178357984

What OpenQuestion holds

Textmetadata
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.