Oxidative Stress-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Responses in Patients With LUAD

Sun, Xinti; Huang, Xingqi; Sun, Xiaofeng; Chen, Si; Zhang, Zeyang; Yu, Yang; Zhang, Peng

doi:10.3389/fgene.2022.909797

Cited by 6 publications

(4 citation statements)

References 60 publications

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“…LncRNA has been involved in tumorigenesis, disease progression, and metastasis of THCA [31][32]. Oxidative stress and lncRNAs have been reported to be associated with the prognosis of cancers such as esophageal cancer and lung cancer [33][34]. So we consider the prognostic risk model of THCA based on oxidative stress and lncRNA has high clinical guiding signi cance for running through the whole process of tumorigenesis and development.…”

Section: Discussionmentioning

confidence: 96%

An oxidative stress-related lncRNA prognostic risk model for thyroid cancer

龙

Zhang

et al. 2023

Preprint

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Purpose: Oxidative stress-elated genes (OSRGs) and long non-coding RNA (lncRNA) have been reported to be associated with cancer prognosis, but the prognostic role of oxidative stress-related lncRNAs (OSRlncRNAs) in thyroid cancer (THCA) is unclear. Methods:RNA-sequencing data and OSRGs were downloaded from public databases. Differentially expressed OSRGs (DE-OSRGs) were identified by limma or DESeq2 packages. Pearson correlation analysis was performed to screen OSRlncRNAs. Furthermore, prognostic risk model was constructed by Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses. A nomogram was further developed. Moreover, functional enrichment analyses were performed on differentially expressed genes (DEGs) between high- and low-risk groups. Finally, a lncRNA-mRNA co-expression network was constructed to analyze the regulatory relationship of model lncRNA. Results: The prognostic risk model based on two OSRlncRNA (DPP4-DT, SAP30-DT) was constructed. The predictive power of the nomogram was accurate and reliable (c-index = 0.942). The neuroactive ligand-receptor interactions, thyroid hormone synthesis, and cytokine-cytokine receptor interactions pathways are important in THCA. The co-expression network results showed that 88 DEGs were regulated by DPP4-DT. Conclusion:The prognostic risk model constructed based on two OSRlncRNA (DPP4-DT, SAP30-DT) could effectively predict the prognosis of THCA patients and provided insights for new personalized prediction and treatment for THCA patients.

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Section: Discussionmentioning

confidence: 96%

An oxidative stress-related lncRNA prognostic risk model for thyroid cancer

龙

Zhang

et al. 2023

Preprint

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“…Figure 1 describes the design of this study. OS-related genes were extracted from the GeneCards database (v5.10, https://www.genecards.org ) using the keyword “oxidative stress” with a relevance score ≥ 7 according to previous methods [ 26 – 28 ]. Six publicly available transcriptome datasets containing intestinal biopsies from patients with CD and healthy controls (HCs) were obtained from the Gene Expression Omnibus (GEO) database [ 29 – 39 ] and meta-analyzed to identify differentially expressed genes (DEGs) related to OS in CD.…”

Section: Methodsmentioning

confidence: 99%

Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn’s disease: a multi-omics Mendelian randomization study

Zhang

et al. 2023

BMC Med

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Background Oxidative stress (OS) is a key pathophysiological mechanism in Crohn’s disease (CD). OS-related genes can be affected by environmental factors, intestinal inflammation, gut microbiota, and epigenetic changes. However, the role of OS as a potential CD etiological factor or triggering factor is unknown, as differentially expressed OS genes in CD can be either a cause or a subsequent change of intestinal inflammation. Herein, we used a multi-omics summary data-based Mendelian randomization (SMR) approach to identify putative causal effects and underlying mechanisms of OS genes in CD. Methods OS-related genes were extracted from the GeneCards database. Intestinal transcriptome datasets were collected from the Gene Expression Omnibus (GEO) database and meta-analyzed to identify differentially expressed genes (DEGs) related to OS in CD. Integration analyses of the largest CD genome-wide association study (GWAS) summaries with expression quantitative trait loci (eQTLs) and DNA methylation QTLs (mQTLs) from the blood were performed using SMR methods to prioritize putative blood OS genes and their regulatory elements associated with CD risk. Up-to-date intestinal eQTLs and fecal microbial QTLs (mbQTLs) were integrated to uncover potential interactions between host OS gene expression and gut microbiota through SMR and colocalization analysis. Two additional Mendelian randomization (MR) methods were used as sensitivity analyses. Putative results were validated in an independent multi-omics cohort from the First Affiliated Hospital of Sun Yat-sen University (FAH-SYS). Results A meta-analysis from six datasets identified 438 OS-related DEGs enriched in intestinal enterocytes in CD from 817 OS-related genes. Five genes from blood tissue were prioritized as candidate CD-causal genes using three-step SMR methods: BAD, SHC1, STAT3, MUC1, and GPX3. Furthermore, SMR analysis also identified five putative intestinal genes, three of which were involved in gene–microbiota interactions through colocalization analysis: MUC1, CD40, and PRKAB1. Validation results showed that 88.79% of DEGs were replicated in the FAH-SYS cohort. Associations between pairs of MUC1–Bacillus aciditolerans and PRKAB1–Escherichia coli in the FAH-SYS cohort were consistent with eQTL–mbQTL colocalization. Conclusions This multi-omics integration study highlighted that OS genes causal to CD are regulated by DNA methylation and host-microbiota interactions. This provides evidence for future targeted functional research aimed at developing suitable therapeutic interventions and disease prevention.

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“…For example, LINC00092 directly binds to 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 to promote tumorigenesis ( 18 ). Sun et al ( 19 ) constructed a prognostic risk model to predict Lung adenocarcinoma patient's survival and response to immunotherapy based on hub oxidative stress-related lncRNAs using bioinformatics analysis. As a result of these newly identified features, prognosis can be more accurately predicted and a greater understanding of how lncRNAs contribute to cancer development can be gained.…”

Section: Introductionmentioning

confidence: 99%

Glycolysis‑related lncRNA may be associated with prognosis and immune activity in grade II‑III glioma

Yang,

Zhang,

Cui

et al. 2024

Oncol Lett

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Glucose metabolism, as a novel theory to explain tumor cell behavior, has been intensively studied in various tumors. The present study explored the long non-coding RNAs (lncRNAs) related to glycolysis in grade II–III glioma, aiming to provide a promising target for further research. Pearson correlation analysis was used to identify glycolysis-related lncRNAs. Univariate/multivariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator algorithm were applied to identify glycolysis-related lncRNAs to construct a prognosis prediction model. Subsequently, multi-dimensional evaluations were used to verify whether the risk model could predict the prognosis and survival rate of patients with grade II–III glioma. Finally, it was verified by functional experiments. The present study finally identified seven glycolysis-related lncRNAs (CRNDE, AC022034.1, RHOQ-AS1, AL159169.2, AL133215.2, AC007098.1 and LINC02587) to construct a prognosis prediction model. The present study further investigated the underlying immune microenvironment, somatic landscape and functional enrichment pathways. Additionally, individualized immunotherapeutic strategies and candidate compounds were identified to guide clinical treatment. The experimental results demonstrated that CRNDE could increase the proliferation of SHG-44 cells. In conclusion, a large sample of human grade II–III glioma in The Cancer Genome Atlas database was used to construct a risk model using glycolysis-related lncRNAs to predict the prognosis of patients with grade II–III glioma.

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Oxidative Stress-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Responses in Patients With LUAD

Cited by 6 publications

References 60 publications

An oxidative stress-related lncRNA prognostic risk model for thyroid cancer

An oxidative stress-related lncRNA prognostic risk model for thyroid cancer

Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn’s disease: a multi-omics Mendelian randomization study

Glycolysis‑related lncRNA may be associated with prognosis and immune activity in grade II‑III glioma

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