Evidence map›Paper›PMID 35611195›Full record

ArticleWorld journal of clinical cases2022

Autophagy-related long non-coding RNA prognostic model predicts prognosis and survival of melanoma patients.

Yue Qiu, Hong-Tao Wang, Xi-Fan Zheng, Xing Huang, Jin-Zhi Meng, Jun-Pu Huang, Zhen-Pei Wen, Jun Yao

Open access · diamondAbstract read
In one paragraph

Article in World journal of clinical cases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.9field-weighted citation impact, top 28% 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

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. Vitamin D Reprograms Non-Coding RNA Networks to Block Zika Virus in Human Macrophages.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
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 at 2 institutions in 1 country.

Yue QiuDepartment of Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530000, Guangxi Zhuang Autonomous Region, China.
Hong-Tao WangDepartment of Orthopedics, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning 530000, Guangxi Zhuang Autonomous Region, China.
Xi-Fan ZhengDepartment of Bone and Joint Surgery, Guangxi Medical University, Nanning 530000, Guangxi Zhuang Autonomous Region, China.
Xing HuangDepartment of Bone and Joint Surgery, Guangxi Medical University, Nanning 530000, Guangxi Zhuang Autonomous Region, China.
Jin-Zhi MengDepartment of Bone and Joint Surgery, Guangxi Medical University, Nanning 530000, Guangxi Zhuang Autonomous Region, China.
Jun-Pu HuangDepartment of Bone and Joint Surgery, Guangxi Medical University, Nanning 530000, Guangxi Zhuang Autonomous Region, China.
Zhen-Pei WenDepartment of Bone and Joint Surgery, Guangxi Medical University, Nanning 530000, Guangxi Zhuang Autonomous Region, China.
Jun YaoDepartment of Orthopedics, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning 530000, Guangxi Zhuang Autonomous Region, China. yaojun800524@126.com.
Guangxi Medical University · CNFirst Affiliated Hospital of GuangXi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMelanomas are malignant tumors that can occur in different body parts or tissues such as the skin, mucous membrane, uvea, and pia mater. Long non-coding RNAs (lncRNAs) are key factors in the occurrence and development of many malignant tumors, and are involved in the prognosis of some patients.

aimTo identify autophagy-related lncRNAs in melanoma that are crucial for the diagnosis, treatment, and prognosis of melanoma patients.

methodsWe retrieved transcriptome expression profiles and clinical information of 470 melanoma patients from The Cancer Genome Atlas (TCGA) database. Then, we identified autophagy-related genes in the Human Autophagy Database. Using R, coexpression analysis of lncRNAs and autophagy-related genes was conducted to obtain autophagy-related lncRNAs and their expression levels. We also performed univariate and multivariate Cox proportional risk analyses on the obtained datasets, to systematically evaluate the prognostic value of autophagy-related lncRNAs in melanoma. Fifteen autophagy-related lncRNAs were identified and an autophagy-related prognostic signature for melanoma was established. The Kaplan-Meier and univariate and multivariate Cox regression analyses were used to calculate risk scores. Based on the risk scores, melanoma patients were randomly divided into high- and low-risk groups. Receiver operating characteristic curve analysis, dependent on time, was performed to assess the accuracy of the prognostic model. At the same time, we also downloaded the melanoma data sets GSE65904, GSE19234, and GSE78220 from the GENE EXPRESSION OMNIBUS database for model verification. Finally, we performed Gene Set Enrichment Analysis functional annotation, which showed that the low and the high-risk groups had different enriched pathways.

resultsThe co-expression network for autophagy-related genes was constructed using R, and 936 lncRNAs related to autophagy were identified. Then, 52 autophagy-related lncRNAs were significantly associated with TCGA melanoma patients' survival by univariate Cox proportional risk analysis (

conclusionOverall, the risk model developed based on the 15 autophagy-related lncRNAs can have important prognostic value and may provide autophagy-related clinical targets for melanoma treatment.

Indexed as

AutophagyBioinformaticsLong non-coding RNAsMelanomaPrognosisThe Cancer Genome Atlas

Identifiers

PMID35611195
PMCPMC9048552
OpenAlexW4226216157

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.