LncRNAs act as prognostic biomarkers in gastric cancer: A systematic review and meta-analysis

The prognostic, diagnostic, and therapeutic impact of Long noncoding RNAs in gastric cancer

Gastric cancer (GC), ranking as the third deadliest cancer globally, faces challenges of late diagnosis and limited treatment efficacy. Long non-coding RNAs (lncRNAs) emerge as valuable treasured targets for cancer prognosis, diagnosis, and therapy, given their high specificity, convenient non-invasive detection in body fluids, and crucial roles in diverse physiological and pathological processes. Research indicates the significant involvement of lncRNAs in various aspects of GC pathogenesis, including initiation, metastasis, and recurrence, underscoring their potential as novel diagnostic and prognostic biomarkers, as well as therapeutic targets for GC. Despite existing challenges in the clinical application of lncRNAs in GC, the evolving landscape of lncRNA molecular biology holds promise for advancing the survival and treatment outcomes of gastric cancer patients. This review provides insights into recent studies on lncRNAs in gastric cancer, elucidating their molecular mechanisms and exploring the potential clinical applications in GC.

A meta-analysis of the clinicopathological significance of the lncRNA MALAT1 in human gastric cancer

Background

Dysregulation of the long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) has been linked to some oncogenic pathways that induce cancer initiation and progression. This meta-analysis was conducted to specifically summarize the most recent research on MALAT1 function in human gastric cancer (GC).

Methods

The eligible studies were first identified by searching HowNet, Web of Science, PubMed, The Cochrane Library, Embase, and Nature databases for studies published as of April 1, 2023. The meta-analysis included 14 studies assessing MALAT1 expression and presenting clinical parameters and survival outcomes.

Results

The results illustrated that high MALAT1 expression is predictive of lymph node metastasis (pooled odds ratio [OR] = 2.99, 95% confidence interval [CI] = 1.97-4.54, P < 0.001) and distant metastasis in GC (OR = 3.11, 95% CI = 1.68-5.75, P < 0.001). In addition, MALAT1 was associated with GC tumor invasion (T3/T4 vs. T1/T2: OR = 2.90, 95% CI = 1.90- 4.41, P <0.001) and TNM stage (III/IV vs I/II: OR = 2.93, 95% CI: 1.80-4.77, P <0.001). Additionally, higher MALAT-1 expression predicted poorer overall survival in patients with GC (hazard ratio = 1.64, 95% CI = 1.20-2.09, P < 0.001).

Conclusions

The current findings suggest that the high MALAT1 expression is an adverse biomarker for prognostic outcomes, lymph node metastasis, TNM stage, and distant metastasis in GC and MALAT1 could be a prognostic biomarker for GC.

Genetic risk and gastric cancer: polygenic risk scores in population-based case-control study

OBJECTIVE

This study aimed to screen and identify common variants and long noncoding RNA (lncRNA) single nucleotide polymorphisms (SNPs) associated with gastric cancer risk, and construct prediction models based on polygenic risk score (PRS).

METHODS

The risk factors associated with gastric cancer were screened following meta-analysis and bioinformatics, verified by population-based case-control study. We constructed PRS and weighted genetic risk scores (wGRS) derived from the validation data set. Net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to evaluate model.

RESULTS

The PRS was divided into 10 quantiles, with the 40-60% quantile as a reference. A risk gradient was revealed across quantile of the PRS, the risk of gastric cancer in the highest 10 quantile of PRS was 3.24-fold higher than that in control population (OR = 3.24, 95%CI: 2.07, 5.06). For NRI and IDI, PRS combinations were significantly improved compared to wGRS model combinations (P < 0.001). The model of PRS combined with lncRNA SNPs, smoking, drinking and Helicobacter pylori infection was the best-fitting model (AIC = 117.23, BIC = 122.31).

CONCLUSION

The model based on PRS combined with lncRNA SNPs, H. pylori infection, smoking, and drinking had the optimal predictive ability for gastric cancer risk, which was helpful to distinguish high-risk groups.

Clinicopathological and prognostic value of lncRNAs expression in gastric cancer: A field synopsis of observational studies and databases validation

BACKGROUND

The purpose of this study was to evaluate existing evidence in the field of long non-coding RNAs (lncRNAs) and prognosis of gastric cancer.

METHODS

A comprehensive literature search was performed through the electronic database. The combined hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) of overall survival (OS), disease-free survival (DFS), or progression free survival (PFS) were calculated to assess the strength of the association. Kaplan-Meier (KM) plotter was used to verify lncRNA HOX transcript antisense RNA (HOTAIR) expression and OS.

RESULTS

Overall, a significant correlation between high lncRNAs expression and poor OS was explored in patients with gastric cancer (HR = 1.78, P < .001). Subgroup analysis based on statistical methods indicated the high expression of lncRNAs in log-rank (HR = 1.87, P < .001) and multivariate analysis (HR = 1.71, P < .001) were all significantly correlated with the poor OS. Clinicopathological parameters analysis showed the lncRNA expression were significantly associated prognosis, including TNM stage, tumor size, pathological differentiation, lymph nodes metastasis, distance metastasis, invasion depth and Lauren's classification. It was consistent with the verification results of bioinformatics database for lncRNA HOTAIR (P < .001).

CONCLUSION

Our study confirmed the expression of lncRNAs and clinicopathological features may serve as effective indicators of prognosis in patients with gastric cancer.

Focally amplified long non-coding RNA in epithelial cancer as a potential biomarker and therapeutic target

Deregulation of long non-coding RNAs (lncRNAs) has been implicated in tumorigenesis. FALEC is a lncRNA upregulated in multiple cancer types. FALEC functions as an oncogene through various mechanisms, such as competitively binding miRNAs and regulation of PI3K/AKT, Tp53 and phosphatase and tensin homolog signaling pathways. Pertinent to clinical practice, the use of FALEC as a putative biomarker has been identified. These findings suggested that FALEC might play a pivotal role in human cancers. Further studies are warranted to examine the diagnostic and prognostic performance of FALEC as a noninvasive biomarker in liquid biopsy samples and promote its development to be a clinically utilizable prognostic cancer biomarker and molecular therapeutic target.

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

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.

Long non-coding RNA signature in gastric cancer

Gastric cancer as a common human malignancy has been associated with aberrant expressions of several coding and non-coding genes. Long non-coding RNAs (lncRNAs) as regulators of gene expressions at different genomic, transcriptomic and post-transcriptomic levels are among putative biomarkers and therapeutic targets in gastric cancer. In the present study, we have searched available literature and listed lncRNAs that are involved in the pathogenesis of gastric cancer. In addition, we discuss associations between expressions of these lncRNAs and tumoral features or risk factors for gastric cancer. Based on the established role of lncRNAs in regulation of genomic stability, cell cycle, apoptosis, angiogenesis and other aspects of cell physiology, the potential of these transcripts as therapeutic targets in gastric cancer should be evaluated in future studies.