Dataset Information

Polygenic Risk Scores for Prediction of Gastric Cancer Based on Bioinformatics Screening and Validation of Functional lncRNA SNPs

ABSTRACT:

INTRODUCTION:

Single-nucleotide polymorphisms (SNPs) are used to stratify the risk of gastric cancer. However, no study included gastric cancer–related long noncoding RNA (lncRNA) SNPs into the risk model for evaluation. This study aimed to replicate the associations of 21 lncRNA SNPs and to construct an individual risk prediction model for gastric cancer.

METHODS:

The bioinformatics method was used to screen gastric cancer–related lncRNA functional SNPs and verified in population. Gastric cancer risk prediction models were constructed using verified SNPs based on polygenic risk scores (PRSs).

RESULTS:

Twenty-one SNPs were screened, and the multivariate unconditional logistic regression analysis showed that 14 lncRNA SNPs were significantly associated with gastric cancer. In the distribution of genetic risk score in cases and controls, the mean value of PRS in cases was higher than that in controls. Approximately 20.1% of the cases was caused by genetic variation (P = 1.9 × 10−34) in optimal PRS model. The individual risk of gastric cancer in the lowest 10% of PRS was 82.1% (95% confidence interval [CI]: 0.102, 0.314) lower than that of the general population. The risk of gastric cancer in the highest 10% of PRS was 5.75-fold that of the general population (95% CI: 3.09, 10.70). The introduction of family history of tumor (area under the curve, 95% CI: 0.752, 0.69–0.814) and Helicobacter pylori infection (area under the curve, 95% CI: 0.773, 0.702–0.843) on the basis of PRS could significantly improve the recognition ability of the model.

DISCUSSION:

PRSs based on lncRNA SNPs could identify individuals with high risk of gastric cancer and combined with risk factors could improve the stratification.

SUBMITTER: Duan F

PROVIDER: S-EPMC8604006 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGastric cancer (GC), the most commonly diagnosed cancer worldwide with poor 5-year survival rate in advanced stages. Although immune-related and survival-related biomarkers, which typically comprise aberrantly expressed long non-coding RNAs (lncRNAs) and genes, have been identified, there are no reports of immune-related lncRNA pair (IRLP) signatures for GC.MethodsIn this study, we acquired lncRNA expression profiles from The Cancer Genome Atlas (TCGA) and used the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model (iteration = 1000) to develop a IRLP prognostic signature. The area under curve (AUC) was used to assess the prognosis predictive power. The multivariate Cox regression analysis was performed to identify whether this signature was an independent prognostic factor. The immune cell infiltration analysis was performed between the two risk groups. Last, molecular experiments were performed to explore LINC01082 is involved in the development of GC.ResultsWe acquired lncRNA expression profiles and used the LASSO Cox model to develop an 18-IRLP signature with a strong prognostic predictive power. The 5-year AUC values of the training, validation, and overall TCGA datasets were 0.77, 0.86, and 0.80, respectively. The different prognostic outcomes between the high- and low-risk groups were determined using our 18-IRLP signature. Moreover, our 18-IRLP signature was an independent prognostic factor as per the multivariate Cox regression analysis, and showed better prognostic evaluation than the traditional TNM staging system as well as other clinical features. We also found differences in cancer-associated fibroblast and macrophage M2 infiltration and the expression of PD-L1, CTLA4, LAG3, and HLA were also observed between the two risk groups (P < 0.05). Analysis of biological functions revealed that target genes of the lncRNAs in the IRLP signature were enriched in focal adhesion and regulation of actin cytoskeleton. Finally, as one of significant candidates of IRLP signature, overexpression of LINC01082 suppressed the invasion ability of GC cells as well as PD-L1 expression profiles.ConclusionsOur novel 18-IRLP signature provides new insights regarding immunological biomarkers, imparts a better understanding of the tumor immune microenvironment, and can be used for predicting prognosis and evaluating immune response in GC.

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