Evidence map›Paper›PMID 36111294›Full record

ArticleFrontiers in endocrinology2022

Prognostic model based on m6A-associated lncRNAs in esophageal cancer.

Weidong Wang, Danhong Dong, Pengfei Yu, Tong Chen, Ruiqi Gao, Jiangpeng Wei, Zhenchang Mo, Haikun Zhou, Qinchuan Yang, Chao Yue and 3 more

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2022. 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
–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

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.

  1. Review
  2. Review
  3. Review
  4. Editorial: The RNA revolution and cancer.Frontiers in endocrinology · 2024
    Article
  5. Review
  6. Review
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

13 authors.

Weidong WangDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Danhong DongDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Pengfei YuDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Tong ChenDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Ruiqi GaoDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Jiangpeng WeiDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Zhenchang MoDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Haikun ZhouDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Qinchuan YangDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Chao YueDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Xisheng YangDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Xiaohua LiDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Gang JiDepartment of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This research aimed to build an m6A-associated lncRNA prognostic model of esophageal cancer that can be used to predict outcome in esophageal cancer patients. Methods: RNA sequencing transcriptome data and clinical information about patients with esophageal cancer were obtained according to TCGA. Twenty-four m6A-associated genes were selected based on previous studies. m6A-associated lncRNAs were determined through Pearson correlation analysis. Three m6A-associated lncRNA prognostic signatures were built through analysis of the training set using univariate, LASSO, and multivariate Cox regression. To validate the stabilization of the risk signature, Kaplan-Meier and ROC curve analyses were performed on the testing and complete sets. The prognoses of EC patients were predicted quantitatively by building a nomogram. GSEA was conducted to analyze the underlying signaling pathways and biological processes. To identify the underlying mechanisms through which the lncRNAs act, we constructed a PPI network and a ceRNA network and conducted GO and KEGG pathway analyses. EC samples were evaluated using the ESTIMATE algorithm to compute stromal, immune, and estimate scores. The ssGSEA algorithm was used to quantitatively infer immune cell infiltration and immune functions. The TIDE algorithm was performed to simulate immune evasion and predict the response to immunotherapy. Results: We identified and validated an m6A-associated lncRNA risk model in EC that could correctly and reliably predict the OS of EC patients. The ceRNA network, PPI network, and GO and KEGG pathway analyses confirmed and the underlying mechanisms and functions provided enlightenment regarding therapeutic strategies for EC. Immunotherapy responses were better in the low-risk subgroup, and PD-1 and CTLA4 checkpoint immunotherapy benefited the patients in the low-risk subgroup. Conclusions: We constructed a new m6A-related lncRNA prognostic risk model of EC, based on three m6A-related lncRNAs: LINC01612, AC025166.1 and AC016876.2, that can predict the prognoses of EC patients.

Indexed as

Esophageal NeoplasmsRNA, Long NoncodingBiomarkers, TumorCTLA-4 AntigenGene Expression Regulation, NeoplasticHumansPrognosisProgrammed Cell Death 1 ReceptorRare DiseasesBiomarkers, TumorCTLA-4 AntigenProgrammed Cell Death 1 ReceptorRNA, Long Noncodingbioinformaticsesophageal cancerlncRNAm6Aprognostic signature

Identifiers

PMID36111294
PMCPMC9468245

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.