Development and experimental validation of a senescence-related long non-coding RNA signature for prognostic prediction and immune microenvironment characterization in gastric cancer patients.

Abstract

Background: Cellular senescence is considered a new marker of cancer. It has been suggested that long non-coding RNA (lncRNA) can be used to predict the prognosis of cancers. However, it remains to be seen whether the lncRNAs associated with cellular senescence can be used to predict the prognosis of gastric cancer (GC). The present study aimed to develop a novel senescence-related lncRNA signature (SenLncSig) to predict GC prognosis. The SenLncSig model holds promise for enhancing patient stratification, enabling more precise prognostic predictions and facilitating immunotherapy strategies.

Methods: Senescence-associated lncRNAs were identified from RNA expression profiles in The Cancer Genome Atlas (TCGA) database through the construction of a co-expression network linking senescence genes and lncRNAs. A prognostic signature for GC (334 patients from TCGA-STAD data set), comprising the senescence-related lncRNAs, was developed through univariate and multivariate Cox proportional hazards regression analyses. By using the median SenLncSig risk score, the GC patients were categorized into high- and low-risk groups. A Kaplan-Meier analysis and gene set enrichment analysis were conducted, and immune infiltration, the tumor mutation burden (TMB), and pharmacological treatments were compared between the high- and low-risk groups. We used an independent GC cohort (an external cohort of 30 pairs of tumor and non-tumor tissues from the GC patients) and three GC cell lines to conduct a quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis to validate the results.

Results: We established a SenLncSig, a prognostic risk model comprising the following five senescence-associated lncRNAs; AP000695.2, LINC02381, AC005586.1, AP003392.1, and AP001528.2. According to the SenLncSig, high-risk scores were associated with poor overall survival (multivariate Cox proportional hazard ratio: 1.498, 95% confidence interval: 1.294-1.735; P<0.001). The time-dependent receiver operating characteristic curve indicated that the model performed (area under the curve: 0.711). We developed a nomogram incorporating age, gender, grade, stage, T stage, M stage, N stage, and SenLncSig risk score to estimate 1-year, 3-year, and 5-year survival rates. Further, according to the results of the mutation analysis, patients with a high TMB in the high-risk group had the worst prognosis. Interestingly, the high-risk group had a stronger infiltration of regulatory T cells (P<0.001) and M2 macrophage cells (P<0.001), as well as higher tumor immune dysfunction and exclusion scores than the low-risk group. These results might explain why the high-risk group had a worse prognosis. Finally, the qRT-PCR validation revealed that the AP000695.2 and AP003392.1 expression levels were significantly higher in the tumor tissues and GC cell lines than the normal tissues and normal human gastric epithelial cell line, whereas the opposite pattern was found for LINC02381.

Conclusions: The development of the SenLncSig provided a potential tool for improving patient prognosis predictions and offered preliminary insights into predicting the efficacy of GC immunotherapy.