Clinical and Biological Significances of a Ferroptosis-Related Gene Signature in Lung Cancer Based on Deep Learning

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 ( p < 0.001 ), and this was in line with the findings of the inquiry into the Oncomine and TCGA databases. After that, the findings of the immunohistochemistry analysis revealed that the ACSL4 staining was weakened in NSCLC samples in contrast with the normal samples. It was shown that the differential expression of ACSL4 was substantially linked to the stages of cancer, smoking behaviors, and the status of nodal metastases (all p < 0.001 ). According to the findings of the survival analysis, both RFS and OS were favorable among NSCLC patients who had elevated expression of ACSL4. The ferroptosis sensitization in cancer cells may be reestablished with upregulation of ACSL4 through gene transfection. Mechanistically, protein ubiquitination could perform a remarkable function in ACSL4-induced ferroptosis. ACSL4, which has a function in ferroptosis as both a contributor and monitor, was shown to be downregulated in NSCLC. This finding suggests that ACSL4 might function as a helpful diagnostic and prognostic biological marker and might also be considered a novel possible treatment target for NSCLC.

Ferroptosis in lung cancer: a novel pathway regulating cell death and a promising target for drug therapy

“…In addition, ACSL4 was positively correlated with erastin-induced ferroptosis in NSCLC cells [ 96 ]. When ACSL4 is upregulated, lipid peroxidation products and lethal reactive oxygen species accumulate, thus sensitiating lung cancer cells to ferroptosis [ 97 , 98 ]. Therefore, targeting ACSL4 is an effective way to promote ferroptosis in lung cancer.…”

Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021–2023 Literature

“…Deep learning (unspecified/generic) [92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109] This category encompasses general deep learning approaches, which may include a variety of architectures and techniques. These approaches often involve several minor modifications or adaptations to cater to the specificities of the task at hand, without specializing in a particular method or model like the other categories.…”

“…Del Carmen et al [105] identified chromosomal region alterations associated with therapy response in rectal cancer, utilizing a deep-learning-based algorithm for disease-free survival and overall survival prediction. Huang et al [106] explored the roles of immune microenvironment-related elements in hepatitis B virus-related diseases, while Yang et al [107] investigated the role of ACSL4 in non-small cell lung cancer. The studies underline the ability of deep learning to reveal the intricate links between genetic aberrations and cancer prognosis, further enhancing our understanding of the disease's complex mechanisms.…”

Retracted: Clinical and Biological Significances of a Ferroptosis‐Related Gene Signature in Lung Cancer Based on Deep Learning

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: The presence of these indicators undermines our confidence in the integrity of the article's content and we cannot, therefore, vouch for its reliability.…”