A novel senescence-associated LncRNA signature predicts the prognosis and tumor microenvironment of patients with colorectal cancer: a bioinformatics analysis.

Background: Accumulating evidence suggests that cellular senescence promotes tumor formation and that long non-coding RNAs (lncRNAs) expression predicts tumor prognosis. However, senescence-related variables, particularly lncRNAs, are still largely unknown. Therefore, the present study developed a novel senescence-associated lncRNA signature to predict colorectal cancer (CRC) prognosis.

Methods: A co-expression network of senescence-associated mRNAs and lncRNAs was built using RNA-sequence data from The Cancer Genome Atlas (TCGA). By using the prognosis outcomes data of overall survival (OS) and disease-free survival (DFS) from TCGA, we constructed a prognostic senescence-associated lncRNA signature (SenALSig). The OS and DFS were compared between the low- and high- risk groups defined by SenALSig. A single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithm were used to investigate the relationship between the predictive signature and immune status. Finally, the relationship between SenALSig and drug treatment options was investigated. An independent CRC cohort and three CRC cell lines were recruited to perform real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) analysis to validate the results discovered in silico.

Results: A prognostic risk model consisting of six senescence-associated lncRNAs was constructed, including SNHG16, AL590483.1, ZEB1-AS1, AC107375.1, AC068580.3, and AC147067.1. High-risk scores according to the SenALSig were significantly associated with poor OS (hazard ratio =1.218, 95% confidence interval: 1.140-1.301; P<0.001). The model's accuracy was further supported by receiver operating characteristic (ROC) curves (the area under the curve is 0.714) and a principal component analysis (PCA). In univariate and multivariate Cox regression analyses, SenALSig was further found to be a prognostic factor independent of other clinical factors. Furthermore, we discovered that immune checkpoint expression and response to chemotherapy and targeted therapy differed significantly between the SenALSig-stratified high- and low-risk groups. Finally, the qPCR results revealed that the expression levels of the six senescence-associated lncRNAs differed significantly between tumor and normal tissues and between the CRC cell lines and a normal human colon mucosal epithelial cell line.

Conclusions: SenALSig can better predict survival and risk in CRC patients, as well as help develop new anti-cancer treatment strategies for CRC.