Screening process of published literature
A systematic database search of the literature was conducted, including initially pertinent publications regarding the correlation between SNHG15 and cancers, in PubMed (n=36), Web of Science (n=35), Embase (n=75), and the Cochrane Library (n=0). After initially removing duplicates (n=49), the titles and abstracts of the remaining studies (n=97) were assessed. Seventy-two studies were removed due to being irrelevant topics, reviews, case reports, and conference abstracts. Next, 25 full-text articles were assessed for eligibility. Among them, nine were removed due to a focus on the functional exploration of SNHG15, two were excluded due to a lack of prognostic data, and three articles were excluded due to unclear group numbers. Ultimately, 11 articles containing sufficient data of both survival and clinical features were enrolled in our meta-analysis. Figure 1 presents the detailed selection process for qualified publications.
Characteristics of the enrolled studies
Eleven studies performed in China, including a total of 1,087 patients, that were published from 2016 to 2019 were included. Regarding cancer types, three studies explored lung carcinoma, including one lung cancer and two NSCLC, while the others investigated gastric cancer, hepatocellular carcinoma, renal cell carcinoma, pancreatic ductal adenocarcinoma, breast cancer, papillary thyroid cancer, colorectal cancer, and epithelial ovarian cancer. All samples were cancer tissues and adjacent normal tissues, and the detection assay was qRT-PCR in all cases. Patients were classified into high and low SNHG15 expression groups, and most studies used the median expression level as the cut-off value, except for one study which utilised the mean value and one which did not provide a cut-off value. All studies reported OS, while only two referred to DFS and one mentioned PFS. Regarding HR with 95% CI availability, there were five instances where this could be obtained directly from the papers, and for the remaining cohorts, this was retrieved from K-M curves using Engauge Digitiser software. The follow-up time ranged from 40 to 180 months. The quality of the enrolled studies was assessed by NOS, with scores ranging from 6 to 8. The main features of the enrolled studies are listed in Table 1.
Association between SNHG15 expression and clinical outcomes
The correlation between SNHG15 expression and clinical features was investigated by calculating the pooled OR and 95% CI of age, gender, tumour size, TNM stage and LNM.Results indicated that SNHG15 overexpression was not significantly associated with age (<60 vs. ≥60, OR = 0.98, 95% CI: 0.65-1.48, P = 0.912, Figure 2a), gender (male vs. female, OR = 0.95, 95% CI: 0.73-1.25, P = 0.728, Figure 2b), tumour size (large vs. small, OR = 1.88, 95% CI: 0.91-3.89, P= 0.087, Figure 2c). However, a significant association was observed between increased SNHG15 expression and advanced clinical features, including TNM stage (III-IV vs. I-II, OR = 3.01, 95% CI: 2.15-4.23, P < 0.001, Figure 2d) and LNM (positive vs. negative, OR = 3.20, 95% CI: 2.30-4.45, P < 0.001, Figure 2e). Four fixed-effect models and one random-effect model were adopted for the data with low heterogeneity (0%-35.7%) and significant heterogeneity (78.4%), respectively, and the details are shown in Table 2.
To further demonstrate whether SNHG15 could serve as a prognostic predictor in various cancers, we explored the association between elevated SNHG15 expression and survival indicators (OS/DFS). All enrolled studies reported the OS and a forest plot revealed that the pooled HR and 95% CI were 1.95 (1.53-2.49) by using the fixed-effect model (I2 = 0%, P = 0.778), suggesting that SNHG15 overexpression indicated worse OS (P < 0.001, Figure 3a). Similarly, as shown in Figure 3b, no significant heterogeneity in DFS was observed in two studies (I2 = 0%, P = 0.822); therefore, the fixed-effect model was employed. The pooled results revealed that increased SNHG15 expression was significantly associated with worse DFS (HR = 2.31, 95% CI: 1.48-3.61, P < 0.001, Figure 3b). Given that no obvious heterogeneity was observed in the results, we did not perform subgroup analysis. Additionally, we only analysed publication bias for OS given that only two studies reported DFS. More detailed information is provided in Table 2.
Publication bias and sensitivity analysis for prognosis
The pooled HR for OS and DFS was not influenced after removing any single study, one by one, in the sensitivity analysis, indicating the reliability and stability of our results (Figure 4a-b). Furthermore, Begg’s test and Egger’s test (P = 0.938 and P=0.970, respectively) both quantitatively revealed that there was no significant publication bias in OS (Figure 4c-d).
Validation of the results in the GEPIA database
The GEPIA database was used to further validate our results. In terms of SNHG15 dysregulation, SNHG15 overexpression was identified in colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), kidney renal clear cell carcinoma (KIRC), pancreatic adenocarcinoma (PAAD), rectum adenocarcinoma (READ), testicular germ cell tumours (TGCT), and thymoma (THYM) (Figure 5). Regarding the association between SNHG15 expression and prognosis, survival plots assessing 9,502 patients with 33 types of malignancies in the GEPIA cohort divided into high and low expression groups based on median value revealed that SNHG15 upregulation was associated with worse OS and DFS (Figure 6), confirming the results of our meta-analysis. Furthermore, increased SNHG15 expression was correlated with worse OS in adrenocortical carcinoma (ACC), KIRC, mesothelioma (MESO), uveal melanoma (UVM), and worse DFS in ACC, prostate adenocarcinoma (PRAD), UVM (log-rank P < 0.05) (Figures 7-8). These results support our conclusions and indicate that SNHG15 could be a novel prognostic biomarker for various cancers.
Prediction of SNHG15 function
To further understand the molecular mechanism of SNHG15 overexpression affecting the prognosis of various cancers, we predicted its possible biological function and involved signaling pathways of SNHG15 using six online databases. First, the ceRNA regulations for SNHG15 were identified through starBase, LncBase Predicted v.2, miRDB, TargetScan, miRTarBase, mirDIP online prediction, and then a SNHG15-miRNA-mRNA network was constructed by utilizing cytoscape software (Figure 9).