Establishment and Validation of a Ferroptosis-Related Long Non-Coding RNA Signature for Predicting the Prognosis of Stomach Adenocarcinoma

Zhang, Shuqiong; Zheng, Naisheng; Chen, Jiayao; Du, Kun; Yang, Junyao; Shen, Lisong

doi:10.3389/fgene.2022.818306

Cited by 12 publications

(2 citation statements)

References 33 publications

(34 reference statements)

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“…In this study, seven lncRNAs (AC016394.2, AL365181.3, AL590617.2, DEPDC1-AS1, IGFL2-AS1, LINC00511 and LINC00571) were chosen for building a prediction model of disulfidptosis-related lncRNAs. In previous studies, AC016394.2, AL365181.3 and LINC00571 were used as cuproptosis, ferroptosis and cellular senescence-related lncRNAs, respectively, in bioinformatic analyses for the construction of a predictive model for gastric cancer ( 30 - 32 ). Additionally, DEPDC1-AS1 promoted the expansion and spread of human gastric cancer cells using the human antigen R-F27R pathway ( 33 ).…”

Section: Discussionmentioning

confidence: 99%

Establishment and validation of a prognostic scoring model based on disulfidptosis-related long non-coding RNAs in stomach adenocarcinoma

Xing,

Xu,

Zhang

2024

Transl Cancer Res

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Background Stomach adenocarcinoma (STAD), a frequently occurring gastrointestinal tumour, is often detected late and has a poor prognosis. Long non-coding RNAs (lncRNAs) significantly affect tumour development. Recent studies have identified disulfidptosis as a previously unexplained form of cell death. Herein, we aimed to examine the predictive value of disulfidptosis-related lncRNA models for the clinical prognosis and immunotherapy of STAD. Methods STAD-related transcriptomic data were obtained from The Cancer Genome Atlas (TCGA), whereas genes associated with disulfidptosis were identified from previously published papers. A risk prediction model for disulfidptosis-related lncRNAs was developed using the Cox regression and least absolute shrinkage selection algorithm methods. The accuracy of the model was confirmed using calibration curves, and the biological functions were analysed using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). Finally, the tumour mutation burden (TMB) and tumour immune dysfunction and exclusion (TIDE) algorithms were used to screen drugs that are sensitive to STAD. Results The risk prediction models were constructed using seven disulfidptosis-related lncRNAs. The validated results were consistent with the predicted ones, with significant survival differences. When combined with clinical data, the risk scores were used as independent prognostic markers. Based on the tumour mutation load, the high-risk patient group had a poorer survival rate as compared with the low-risk patient group. Further studies were conducted to understand the different groups’ inconsistent responses to immune status; subsequently, relatively sensitive drugs were identified. Conclusions Overall, seven markers of disulfidptosis-related lncRNAs associated with STAD were found to facilitate prognostic prediction, suggesting new ideas for immunotherapy and clinical applications.

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Section: Discussionmentioning

confidence: 99%

Establishment and validation of a prognostic scoring model based on disulfidptosis-related long non-coding RNAs in stomach adenocarcinoma

Xing,

Xu,

Zhang

2024

Transl Cancer Res

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“…In the previous bioinformatics analysis, the Cancer Genome Atlas (TCGA) database suggests that AP000695.2 can be related to the malignant prognosis of various cancers. For example, some studies speculate that AP000695.2 may be involved in the construction of prognosis models of the gastric cancer [ 6 ‒ 8 ] and lung adenocarcinoma [9] . However, the role and underlying of AP000695.2 in LUAD remains unclear and deserves further study.…”

Section: Introductionmentioning

confidence: 99%

LncRNA AP000695.2 promotes glycolysis of lung adenocarcinoma via the miR-335-3p/TEAD1 axis

Xu,

Cheng,

et al. 2023

ABBS

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AP000695.2 is a novel long non-coding RNA (lncRNA). Its aberrant high expression is remarkably associated with poor prognosis of patients with lung adenocarcinoma (LUAD). However, its role and underlying mechanism in LUAD remains unclear. Previous bioinformatics analysis indicated that AP000695.2 may be closely related to the glycolysis of LUAD. This study aims to verify and explore the mechanism of AP000695.2 in glycolysis of LUAD. Overexpression plasmid and siRNA are used to construct cell models of upregulation and downregulation of AP000695.2, respectively. AP000695.2 is highly expressed in lung cancer cell lines as revealed by qPCR. Western blot analysis, FDG uptake, lactate production assay and ECAR determination results show that high expression of AP000695.2 facilitates glycolysis of LUAD cells. CCK-8, EdU staining, Transwell and wound healing assays show that high expression of AP000695.2 promotes cell growth and migration of LUAD. The relationship between AP000695.2 and miR-335-3p is confirmed by bioinformatics analysis and dual-luciferase reporter assays. Through the dual-luciferase reporter assay, TEA domain transcription factor 1 (TEAD1) is identified as a target gene of miR-335-3p. Rescue experiments are applied to verify the relationship among AP000695.2, miR-335-3p and TEAD1. Our study indicates that AP000695.2 is involved in the mechanism of LUAD through functioning as a ceRNA to competitively sponge miR-335-3p, thereby regulating the expression of TEAD1. In the in vivo models, AP000695.2 depletion restrains tumor growth and glycolysis. AP000695.2 promotes the glycolysis of LUAD by regulating the miR-335-3p/TEAD1 axis, and it may serve as a potential target of anti-tumor energy metabolism therapy.

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Construction of a pancreatic cancer prediction model for oxidative stress-related lncRNA

Huang

Wei

Yao

et al. 2023

Funct Integr Genomics

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Long non-coding RNAs (lncRNAs) may play a role in oxidative stress by altering the tumor microenvironment, thereby affecting pancreatic cancer progression. There is currently limited information on oxidative stress-related lncRNAs as novel prognostic markers of pancreatic cancer. Gene expression and clinical data of patients with pancreatic cancer were downloaded from The Cancer Genome Atlas (TCGA-PAAD) and the International Cancer Genome Consortium (ICGC-PACA) database. A weighted gene co-expression network analysis (WGCNA) was constructed to identify genes that were differentially expressed between normal and tumor samples. Based on the TCGA-PAAD cohort, a prediction model was established using lasso regression and Cox regression. The TCGA-PAAD and ICGC-PACA cohorts were used for internal and external validation, respectively. Furthermore, a nomogram based on clinical characteristics was used to predict mortality of patients. Differences in mutational status and tumor-infiltrating immune cells between risk subgroups were also explored and model-based lncRNAs were analyzed for potential immune-related therapeutic drugs. A prediction model for 6-lncRNA was established using lasso regression and Cox regression. Kaplan–Meier survival curves and receiver operating characteristic (ROC) curves indicated that patients with lower risk scores had a better prognosis. Combined with Cox regression analysis of clinical features, risk score was an independent factor predicting overall survival of patients with pancreatic cancer in both the TCGA-PAAD and ICGC-PACA cohorts. Mutation status and immune-related analysis indicated that the high-risk group had a significantly higher gene mutation rate and a higher possibility of immune escape, respectively. Furthermore, the model genes showed a strong correlation with immune-related therapeutic drugs. A pancreatic cancer prediction model based on oxidative stress-related lncRNA was established, which may be used as a biomarker related to the prognosis of pancreatic cancer to evaluate the prognosis of pancreatic cancer patients.

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Establishment and Validation of a Ferroptosis-Related Long Non-Coding RNA Signature for Predicting the Prognosis of Stomach Adenocarcinoma

Cited by 12 publications

References 33 publications

Establishment and validation of a prognostic scoring model based on disulfidptosis-related long non-coding RNAs in stomach adenocarcinoma

Establishment and validation of a prognostic scoring model based on disulfidptosis-related long non-coding RNAs in stomach adenocarcinoma

LncRNA AP000695.2 promotes glycolysis of lung adenocarcinoma via the miR-335-3p/TEAD1 axis

Construction of a pancreatic cancer prediction model for oxidative stress-related lncRNA

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