Evidence map›Paper›PMID 38539235›Full record

ArticleJournal of translational medicine2024

Characterizing and forecasting neoantigens-resulting from MUC mutations in COAD.

Min Chen, Xin Zhang, Zihe Ming, Lingyu, Xiaorong Feng, Zhenguo Han, Han-Xiang An

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.0field-weighted citation impact, top 12% 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

4 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

7 authors at 4 institutions in 1 country.

Min ChenClinical Central Research Core, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi, China. mchenst@outlook.com.
Xin ZhangThe Center Laboratory, Shanghai Medical College, Zhongshan Hospital (Xiamen Affiliated) of Fudan University, Fudan University, Xiamen, China.
Zihe MingCancer Center and Department of Breast and Thyroid Surgery, School of Medicine, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, China.
Lingyu *Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi, China.
Xiaorong FengDepartment of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Chemistry and Chemical Engineering Guangdong Laboratory, Shantou University, Guangdong, China.
Zhenguo HanDepartment of Colorectal and Anal Surgery, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi, China.
Han-Xiang AnClinical Central Research Core, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi, China. anhanxiang@hotmail.com.ORCID 0000-0002-3453-9149
Shanxi Medical University · CNFudan University · CNShantou University · CNXiamen University · CN

Funding

Natural Science Foundation Committee of China 82172009Natural Science Foundation for Young Scientists of Shanxi Province 202303021221200Natural Science Foundation of Shanxi Province 2023GZRZ04
6 · The paper itself

Abstract

backgroundThe treatment for colon adenocarcinoma (COAD) faces challenges in terms of immunotherapy effectiveness due to multiple factors. Because of the high tumor specificity and immunogenicity, neoantigen has been considered a pivotal target for cancer immunotherapy. Therefore, this study aims to identify and predict the potential tumor antigens of MUC somatic mutations (MUCmut) in COAD.

methodsThree databases of TCGA, TIMER2.0, and cBioPortal were used for a detailed evaluation of the association between MUCmut and multi-factors like tumor mutation burden (TMB), microsatellite instability (MSI), prognosis, and the tumor microenvironment within the context of total 2242 COAD patients. Next, TSNAdb and the differential agretopicity index (DAI) were utilized to predict high-confidence neopeptides for MUCmut based on 531 COAD patients' genomic information. DAI was calculated by subtraction of its predicted HLA binding affinity of the MUCmut peptide from the corresponding wild-type peptide.

resultsThe top six mutation frequencies (14 to 2.9%) were from MUC16, MUC17, MUC5B, MUC2, MUC4 and MUC6. COAD patients with MUC16 and MUC4 mutations had longer DFS and PFS. However, patients with MUC13 and MUC20 mutations had shorter OS. Patients with the mutation of MUC16, MUC5B, MUC2, MUC4, and MUC6 exhibited higher TMB and MSI. Moreover, these mutations from the MUC family were associated with the infiltration of diverse lymphocyte cells and the expression of immune checkpoint genes. Through TSNAdb 1.0/NetMHCpan v2.8, 452 single nucleotide variants (SNVs) of MUCmut peptides were identified. Moreover, through TSNAdb2.0/NetMHCpan v4.0, 57 SNVs, 1 Q-frame shift (TS), and 157 short insertions/deletions (INDELs) of MUCmut were identified. Finally, 10 high-confidence neopeptides of MUCmut were predicted by DAI.

conclusionsTogether, our findings establish the immunogenicity and therapeutic potential of mutant MUC family-derived neoantigens. Through combining the tools of TSNAdb and DAI, a group of novel MUCmut neoantigens were identified as potential targets for immunotherapy.

Indexed as

AdenocarcinomaColonic NeoplasmsAntigens, NeoplasmCA-125 AntigenHumansMutationPeptidesTumor MicroenvironmentAntigens, NeoplasmCA-125 AntigenPeptidesBioinformaticsColon adenocarcinomaImmunotherapyMUCTumor neoantigens

Identifiers

PMID38539235
PMCPMC10967086
OpenAlexW4393233051

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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.