ArticleComputational and mathematical methods in medicine2022
Clinical and Biological Significances of a Ferroptosis-Related Gene Signature in Lung Cancer Based on Deep Learning.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature.Biology · 2023Article
- Ferroptosis in lung cancer: a novel pathway regulating cell death and a promising target for drug therapy.Cell death discovery · 2023Review
- Retracted: Clinical and Biological Significances of a Ferroptosis-Related Gene Signature in Lung Cancer Based on Deep Learning.Computational and mathematical methods in medicine · 2023Article
Corrections and comments
- Retraction · 2023-07-26Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/ADB0125DE797E6D4C14524405D9DF1
- Retracted
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acyl-CoA synthetase long-chain family member 4 (ACSL4) has been linked to the occurrence of tumors and is implicated in the ferroptosis process. Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, classification of cancer, and prediction of metastasis. Nonetheless, neither the level of ACSL4 expression nor its predictive significance in non-small-cell lung cancer (NSCLC) is well understood at this time. Predictions of the ACSL4 mRNA expressions in NSCLC and its link to NSCLC prognosis were made with the aid of the Oncomine and TCGA databases. By performing real-time PCR, we detected the levels of ACSL4 expression that were present in human NSCLC samples. Analyses of the diagnostic, as well as the prognostic significance of ACSL4 in NSCLC, were performed with the use of Kaplan-Meier curves. To assess the influence of ACSL4 on ferroptosis in NSCLC cell lines, an inducer of ferroptosis, namely, erastin, was utilized in this study. In NSCLC tissues, there was a substantial decrease in the level of ACSL4 expression (
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Registered trials
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.