Immune-Related LncRNAs to Construct a Prognosis Risk-Assessment Model for Gastric Cancer

Zhi, Shilin; Yang, Bin; Zhou, Shengning; Tan, Jianbo; Zhong, Guangyu; Han, Fanghai

doi:10.3390/curroncol29070391

Cited by 4 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the gradual application of tumor immunotherapy in clinical practice, researchers are actively exploring tumor immunotherapy for GC, which is expected to bring hope to GC patients. 36 Tumor immunotherapy aims to reshape the specific immunity against tumors in order to kill them. LncRNAs play a pivotal role in regulating the tumor immune microenvironment.…”

Section: Discussionmentioning

confidence: 99%

Upregulation of OTX2-AS1 is Associated With Immune Infiltration and Predicts Prognosis of Gastric Cancer

Han

Cui

Wang

et al. 2023

Technol Cancer Res Treat

View full text Add to dashboard Cite

Background It is unclear whether the long non-coding RNA (lncRNA) OTX2 antisense RNA 1 (OTX2-AS1) plays a pivotal role in gastric cancer (GC). An analysis of The Cancer Genome Atlas (TCGA) database data and bioinformatics was used to explore the relationship between OTX2-AS1 and GC in the current study. Methods We evaluated the relationship between clinical features and OTX2-AS1 expression, prognostic factors, and the significant involvement of OTX2-AS1 in function using various statistical methods, such as Kaplan–Meier method, Cox regression analysis, Gene Set Enrichment Analysis (GSEA), and immune infiltration analysis. GC cell lines were tested for OTX2-AS1 expression using qRT-PCR. Results A high level of OTX2-AS1 expression was significantly and negatively associated with Helicobacter pylori ( H pylori) infection in GC patients ( P = .006) and predicted a poorer overall survival (OS) (HR: 1.54; 95% CI: 1.10-2.14; P = .011), progression-free interval (PFI) (HR: 1.75; 95% CI: 1.22-2.51; P = .002) and disease-specific survival (DSS) (HR: 1.85; 95% CI: 1.21-2.85; P = .005) in GC patients. There was an independent correlation between OTX2-AS1 expression (HR: 1.771; 95% CI: 1.164-2.696; P = .008) and OS in patients with GC. There were differential enrichments for the OTX2-AS1 high expression phenotype in the olfactory transduction, G alpha (s) signaling events, keratinization, olfactory signaling pathway, and preimplantation embryo. OTX2-AS1 expression may be related to certain immune-infiltrating cells. Compared to gastric epithelial cells (GES-1), GC cell lines showed a significant increase in OTX2-AS1 expression. Conclusion There was a significant association between OTX2-AS1 expression in GC patients and poor survival, suggesting that it may be a useful biomarker for prognosis and immunotherapy outcome of stomach adenocarcinoma (STAD) in GC.

show abstract

Section: Discussionmentioning

confidence: 99%

Upregulation of OTX2-AS1 is Associated With Immune Infiltration and Predicts Prognosis of Gastric Cancer

Han

Cui

Wang

et al. 2023

Technol Cancer Res Treat

View full text Add to dashboard Cite

show abstract

“…We explored differentially expressed genes in high- and low-risk groups using the R package (limma) with |log 2 FC| ≥ 1 and FDR < 0.05 as filters [ 24 ]. Then, the enrichment of differential genes was explored using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) with a filter condition of p < 0.05.…”

Section: Methodsmentioning

confidence: 99%

In Silico Identification and Validation of Cuproptosis-Related LncRNA Signature as a Novel Prognostic Model and Immune Function Analysis in Colon Adenocarcinoma

et al. 2022

View full text Add to dashboard Cite

Background: Colon adenocarcinoma (COAD) is the most common subtype of colon cancer, and cuproptosis is a recently newly defined form of cell death that plays an important role in the development of several malignant cancers. However, studies of cuproptosis-related lncRNAs (CRLs) involved in regulating colon adenocarcinoma are limited. The purpose of this study is to develop a new prognostic CRLs signature of colon adenocarcinoma and explore its underlying biological mechanism. Methods: In this study, we downloaded RNA-seq profiles, clinical data and tumor mutational burden (TMB) data from the TCGA database, identified cuproptosis-associated lncRNAs using univariate Cox, lasso regression analysis and multivariate Cox analysis, and constructed a prognostic model with risk score based on these lncRNAs. COAD patients were divided into high- and low-risk subgroups based on the risk score. Cox regression was also used to test whether they were independent prognostic factors. The accuracy of this prognostic model was further validated by receiver operating characteristic curve (ROC), C-index and Nomogram. In addition, the lncRNA/miRNA/mRNA competing endogenous RNA (ceRNA) network and protein–protein interaction (PPI) network were constructed based on the weighted gene co-expression network analysis (WGCNA). Results: We constructed a prognostic model based on 15 cuproptosis-associated lncRNAs. The validation results showed that the risk score of the model (HR = 1.003, 95% CI = 1.001–1.004; p < 0.001) could serve as an independent prognostic factor with accurate and credible predictive power. The risk score had the highest AUC (0.793) among various factors such as risk score, stage, gender and age, also indicating that the model we constructed to predict patient survival was better than other clinical characteristics. Meanwhile, the possible biological mechanisms of colon adenocarcinoma were explored based on the lncRNA/miRNA/mRNA ceRNA network and PPI network constructed by WGCNA. Conclusion: The prognostic model based on 15 cuproptosis-related lncRNAs has accurate and reliable predictive power to effectively predict clinical outcomes in colon adenocarcinoma patients.

show abstract

“…Subsequently, a risk score model was built based on multivariate Cox regression analyses that showed the altered status of the DNAm-driven gene signature was significantly associated with the overall survival (OS) of patients with GC. Shilin Zhi et al (2022) [14] screened the immune-related lncRNAs from TCGA database and identified 13 lncRNAs to construct a prognosis assessment model that assigned the patients to different risk groups and was expected to provide a novel target for immunotherapy in patients with GC. Zepang Sun et al (2020) [80] revealed a novel prognostic factor by a multivariate analysis, a genomics score (GS) comprising 7 miRNAs, 8 mRNA, and 19 DNA methylation sites, which provides additional precision in stratifying patients with different OS prognoses.…”

Section: Omics Data (Excluding Radiomics)mentioning

confidence: 99%

Critical Analysis of Risk Factors and Machine-Learning-Based Gastric Cancer Risk Prediction Models: A Systematic Review

Fan

Miao

et al. 2023

Processes

View full text Add to dashboard Cite

The gastric cancer risk prediction model used for large-scale gastric cancer screening and individual risk stratification is an artificial intelligence tool that combines clinical diagnostic data with a classification algorithm. The ability to automatically make a quantitative assessment of complex clinical data contributes to increased accuracy for diagnosis with higher efficiency, significantly reducing the incidence of advanced gastric cancer. Previous studies have explored the predictive performance of gastric cancer risk prediction models, as well as the predictive factors and algorithms between each model, but have reached controversial conclusions. Thus, the performance of current machine-learning-based gastric cancer risk prediction models alongside the clinical relevance of different predictive factors needs to be evaluated to help build more efficient and feasible models in the future. In this systematic review, we summarize the current research progress related to the gastric cancer risk prediction model; discuss the predictive factors and methods used to construct the model; analyze the role of important predictive factors in gastric cancer, the preference of the selected classification algorithm, and the emphasis of evaluation criteria; and provide suggestions for the subsequent construction and improvement of the gastric cancer risk prediction model. Finally, we propose an improved approach based on the ethical issues of artificial intelligence in medicine to realize the clinical application of the gastric cancer risk prediction model in the future.

show abstract

Immune-Related LncRNAs to Construct a Prognosis Risk-Assessment Model for Gastric Cancer

Cited by 4 publications

References 42 publications

Upregulation of OTX2-AS1 is Associated With Immune Infiltration and Predicts Prognosis of Gastric Cancer

Upregulation of OTX2-AS1 is Associated With Immune Infiltration and Predicts Prognosis of Gastric Cancer

In Silico Identification and Validation of Cuproptosis-Related LncRNA Signature as a Novel Prognostic Model and Immune Function Analysis in Colon Adenocarcinoma

Critical Analysis of Risk Factors and Machine-Learning-Based Gastric Cancer Risk Prediction Models: A Systematic Review

Contact Info

Product

Resources

About