Evidence map›Paper›PMID 36551318›Full record

ArticleBiomolecules2022

Identification and Validation of Cuproptosis-Related LncRNA Signatures in the Prognosis and Immunotherapy of Clear Cell Renal Cell Carcinoma Using Machine Learning.

Zhixun Bai, Jing Lu, Anjian Chen, Xiang Zheng, Mingsong Wu, Zhouke Tan, Jian Xie

Abstract read
In one paragraph

Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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.

Zhixun BaiOrgan Transplant Center, The Affiliated Hospital of Zunyi Medical University, Zunyi 563006, China.ORCID 0000-0003-4352-0658
Jing LuDepartment of Clinical, Zunyi Medical and Pharmaceutical College, Zunyi 563000, China.
Anjian ChenDepartment of Urology, The Affiliated Hospital of Zunyi Medical University, Zunyi 563006, China.
Xiang ZhengDepartment of Medical Genetics, Zunyi Medical University, Zunyi 563006, China.
Mingsong WuDepartment of Medical Genetics, Zunyi Medical University, Zunyi 563006, China.
Zhouke TanOrgan Transplant Center, The Affiliated Hospital of Zunyi Medical University, Zunyi 563006, China.
Jian XieDepartment of Medical Genetics, Zunyi Medical University, Zunyi 563006, China.

Funding

National Natural Science Foundation of China 82260106Natural Science and Technology Foundation of Guizhou Province QianKeHeZhiCheng [2022]YiBan182Natural Science and Technology Foundation of Guizhou Province QKHRCPT [2019] No.1332Technological Project of Zunyi Science and Technology Bureau HZ-2021- No. 218Technological Project of Zunyi Science and Technology Bureau HZ-2021-No. 298The Project of Guizhou Provincial Health Commission gzwkj 2021-138The Project of Guizhou Provincial Health Commission gzwkj2021-517The Project of Guizhou Provincial Health Commission gzwkj2021-539The Special Project of Innovation and Exploration in Zunyi Medical University (2020-024)The Young Scientific and Technological Talents Growth Project of Guizhou Provincial Department of Education QianJiaoHe KY Zi[2022]280 Hao
6 · The paper itself

Abstract

(1) Objective: We aimed to mine cuproptosis-related LncRNAs with prognostic value and construct a corresponding prognostic model using machine learning. External validation of the model was performed in the ICGC database and in multiple renal cancer cell lines via qPCR.

Indexed as

ApoptosisCarcinoma, Renal CellKidney NeoplasmsRNA, Long NoncodingCopperHumansImmunotherapyMachine LearningTumor MicroenvironmentCopperlong non-coding RNA FOXD2-AS1, humanRNA, Long NoncodingccRCCcuproptosisimmunotherapylncRNATMB score

Identifiers

PMID36551318
PMCPMC9776244

What OpenQuestion holds

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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.