Evidence map›Paper›PMID 36545126›Full record

ArticleJournal of oncology2022

A 5-Pathway Signature Predicts Prognosis Based on Immune-Derived lncRNAs in Patients with Breast Cancer.

Bo Liu, Nian Zhu, Huixia Huo, Junqi Long, Xinchan Ji, Jinmeng Li, Xujie Zhuang, Huina Wang, Lujia Li, Yuhaoran Chen and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

12 authors.

Bo LiuSchool of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.ORCID https://orcid.org/0000-0002-2393-117X
Nian ZhuSchool of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.ORCID https://orcid.org/0000-0002-3722-1806
Huixia HuoAffiliated Hospital of Weifang Medical University, Weifang 261041, Shandong Province, China.ORCID https://orcid.org/0000-0002-7263-5801
Junqi LongSchool of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.ORCID https://orcid.org/0000-0002-3059-2807
Xinchan JiSchool of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.ORCID https://orcid.org/0000-0001-9300-2571
Jinmeng LiSchool of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.ORCID https://orcid.org/0000-0002-7613-9506
Xujie ZhuangSchool of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.ORCID https://orcid.org/0000-0002-4514-8741
Huina WangSchool of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.ORCID https://orcid.org/0000-0001-9798-0145
Lujia LiSchool of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.ORCID https://orcid.org/0000-0003-3263-2590
Yuhaoran ChenSchool of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.ORCID https://orcid.org/0000-0002-5932-059X
Wei YangDepartment of Thoracic Surgery, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing 101149, China.ORCID https://orcid.org/0000-0003-4306-6010
Shuangtao ZhaoDepartment of Thoracic Surgery, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing 101149, China.ORCID https://orcid.org/0000-0002-8624-7834

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Currently, predictive models were not developed based on the signaling pathway signatures of immune-related lncRNAs in breast cancer (BRCA) patients. Methods: We selected unsupervised hierarchical clustering algorithm to classify patients with BRCA based on the significant immune-derived lncRNAs from the TCGA dataset. And different methods including ESTIMATE, ImmuneCellAI, and CIBERSORT were performed to evaluate the immune infiltration of tumor microenvironment. Using Lasso regression algorithm, we filtered the significant signaling pathways enriched by GSEA, GSVA, or PPI analysis to develop a prognostic model. And a nomogram integrated with clinical factors and significant pathways was constructed to predict the precise probability of overall survival (OS) of BRCA patients in the TCGA dataset ( Results: BRCA patients were stratified into the PC ( Conclusion: This 5-pathway signature correlated with immune-derived lncRNAs was able to precisely predict the prognosis for patients with BRCA and provided a rich source characterizing immune-related lncRNAs and further informed strategies to target BRCA vulnerabilities.

Identifiers

PMID36545126
PMCPMC9763012

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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.