The Prognostic Value of LncRNA SLNCR1 in Cancers: A Meta-Analysis

Objective This meta-analysis was performed to identify the prognostic value of SLNCR1 in multiple cancer types. Methods Electronic databases, including PubMed, EMBASE, and Web of Science, Cochrane Library, Medline, BioMed Central, Springer, Science Direct, and China National Knowledge Internet (CNKI), were searched for relevant studies up to August 2021, and the hazard ratios (HR) and 95% confidence intervals (95% CI) were calculated to assess the relationship between SLNCR1 expression and overall survival (OS). Results 12 studies with a total of 1155 patients with 9 different types of cancers were included in this meta-analysis. The pooled HR indicates that high SLNCR1 expression represented poorer prognosis of cancer (HR = 2.11, 95% CI: 1.59–2.80, I2 = 0%, P < 0.00001). Additionally, high SLNCR1 expression was correlated with TNM stage (odds ratio (OR): 1.72, 95% CI: 1.08–2.74, I2 = 62%, P=0.02), lymph node metastasis (LNM) (OR:2.42, 95% CI: 1.61–3.64, I2 = 55%, P < 0.0001), and distant metastases (DM) (OR: 2.30, 95% CI: 1.50–3.55, I2 = 27%, P=0.0002). However, no evidence was found for a relationship between SLNCR1 expression and clinical features such as tumor size (OR: 1.71, 95% CI: 0.93–3.14, I2 = 71%, P=0.09), age (OR: 0.86, 95% CI: 0.68–1.08, I2 = 0%, P=0.19), or gender (OR: 1.07, 95% CI: 0.64–1.81, I2 = 55%, P=0.79). Conclusion Our findings found that high SLNCR1 expression was associated with poor OS, advanced tumor stage, tumor size, LNM, and DM in multiple cancers, indicating that SLNCR1 may serve as a potential prognostic biomarker for cancer patients in China.


Introduction
Cancer is expected to rank as the leading cause of death and the most significant barrier to extend human life expectancy worldwide. e incidence and mortality of cancer are expected to grow rapidly with increases in population and age [1]. Although significant achievements have been made in cancer diagnosis and treatment, the five-year survival rate remains dismally low. Numerous scientists remain dedicated to find effective biomarkers for cancer patients [2].
Long noncoding RNA (LncRNA), a novel class of noncoding RNA, commonly refers to RNA transcripts greater than 200 nucleotides in length [3]. Moreover, accumulating studies indicate that LncRNAs may participate in a wide range of biological functions and act as oncogenes or tumor suppressors in cancer evolution [4,5].
erefore, this meta-analysis was performed to bridge this gap in knowledge between the expression of SLNCR1 and prognosis in different kinds of cancers.

Literature Search Strategy and Selection.
Two of the authors conducted a systematic search of published studies to identify relevant articles on the association of SLNCR1 expression with the prognosis of cancer. English language databases, including PubMed, EMBASE, Web of Science, Cochrane Library, Medline, BioMed Central, Springer, Science Direct, and China National Knowledge Internet (CNKI), were searched for eligible studies published from inception to August 2021. e search keywords were as follows: "LINC00673" or "Lnc00673" or "lncRNA 00673," "SLNCR1" or "SLNCR," "ERRLR01," "cancers," "prognosis," "survival" "clinicopathologic feature," and "OS (overall survival)." In addition, the references of the relevant studies were screened to avoid omitting any potentially eligible studies. e literature screening and study selection process is shown in Figure 1.

Inclusion and Exclusion Criteria.
e criteria for study inclusion were as follows: (1) case-control studies or cohort studies; (2) cancer was definite diagnosis by pathological examination; (3) studies examining prognostic characteristics of SLNCR1 expression in tumors, and patients were grouped in accordance with high or low SLNCR1 expression levels; (4) studies with sufficient data, including survival outcome, Kaplan-Meier curve, metastasis, and clinical features for statistical analysis.
e criteria for exclusion were as follows: (1) nonhuman studies, letters, case reports, and review articles; (2) studies without prognostic outcomes.

Data Extraction and Quality Assessment.
Two of the authors screened all the eligible studies and completed data extraction independently. e data included the first author's name, publication year, country of origin, cancer type, sample size, age, gender, tumor size, lymph node metastasis (LNM), distant metastasis (DM), TNM stage, cutoff value, and method of detecting SLNCR1. For studies that provided only the Kaplan-Meier curve, Engauge Digitizer version 4.1 was used to extract hazard ratios (HRs) and 95% confidence intervals (95% CIs), and the method described by Tierney et al. was used to obtain survival data [11]. e quality of the included studies was evaluated by the Newcastle-Ottawa Scale [12].

Statistical Analysis.
e strength of the association between SLNCR1 expression and the prognosis of cancer was estimated by calculating HR or odds ratio (OR) and 95% CIs. HR and 95% CIs were extracted from the Kaplan-Meier curves from published studies, and log HR and standard error were used to summarize overall survival (OS). TNM I and II were combined to indicate low tumor stage, and III and IV were combined for representing the advanced tumor stage. e OR was used to estimate the outcome. Tests for heterogeneity assumptions were checked by the Cochran Q statistic and I 2 tests [13]. I 2 < 50% and P > 0.05 indicated no significant heterogeneity across the studies; therefore, a fixed-effect model was used. I 2 > 50% and P < 0.05 denoted strong heterogeneity for which a random-effect model was used for analysis. Funnel plots were utilized to assess potential publication bias. Sensitivity analyses were performed to identify individual study effects that contributed to pooled results and test the results' reliability.

Association between SLNCR1 Expression and OS.
A meta-analysis was performed to estimate the relationship between SLNCR1 expression and OS. HR was extracted from the survival curves in 7 [14,[17][18][19][21][22][23] of the studies. As shown in Figure 2(a), a fixed-effect model was used since no significant heterogeneity was observed (I 2 � 0, P � 0.82). e combined HR was 2.11 (95%nCI: 1.59-2.80, P < 0.00001), revealing that OS in cancers was markedly related to SLNCR1 expression, with the high SLNCR1 expression group displaying poorer OS than the low SLNCR1 expression group. No obvious asymmetry was detected by the shape of the funnel plot ( Figure 2(b)). Sensitivity analysis demonstrated no significant influence by eliminating any single study on the pooled HR, revealing that the results were stable (Figure 2(c)).

Association between SLNCR1 Expression and Clinical
Features.

Publication Bias and Sensitivity Analysis.
e publication bias of this meta-analysis was estimated by Begg's and Egger's tests (Figure 7). No evidence of publication bias was found in the meta-analysis of OS by Begg's (P � 0.54) or Egger's (P � 0.80) test. However, sensitivity analysis by elimination of each study to determine its effect on the calculation of overall risk of disease found that two studies significantly affected the analysis of the relationship between SLNCR1 expression and TNM stage. After omitting the

Discussion
LncRNAs have been regarded as accidental "transcriptional noise" with little function due to lack of protein-coding capability [26]. Recently, accumulating evidence has shown that LncRNAs may regulate genes or miRNA expression and act as oncogenic or tumor suppressors [27,28]         development of high-throughput genome sequencing technologies, LncRNAs have been identified as new biomarkers for the accurate prognosis of various kinds of tumors due to their functions in tumor proliferation, invasion, migration, and metastasis. SLNCR1, a LncRNA with high accuracy prognostic value, has been demonstrated to be associated with tumorigenesis and progression and was initially associated with decreased melanoma patient survival. Brain-specific homeobox protein 3a and androgen receptors bind within SLNCR1's conserved region, activating matrix metalloproteinase 9 and subsequently increasing malignant melanoma invasion [6]. Furthermore, SLNCR1 may regulate cell migration, invasion, and stemness through interactions with secretory sPLA2 in nonsmall cell lung cancer [29]. It has been shown to promote the proliferation of breast cancer by sponging miR-515-5p to regulate MARK4 expression and inhibit the Hippo signaling pathway [23]. An increasing number of studies have explored SLNCR1 interaction partners and biological functions in various types of cancers, but the relationship between the expression of SLNCR1 and tumor progression remains poorly understood. e current study presents the first meta-analysis to evaluate the relationship between SLNCR1 expression and the prognosis of cancers. e results indicate that patients with high expression levels of SLNCR1 tend to have poorer OS than those with low expression levels. In other words, the high expression level of SLNCR1 is a predictor of a negative prognosis of cancer. Meanwhile, subgroup analysis in the fixed model was performed to assess the role of SLNCR1 in digestive system cancers.

Journal of Oncology
e data show that in both digestive and nondigestive system tumors, high SLNCR1 expression was associated with poor prognosis. Furthermore, the results also indicate that tumor stage and the high SLNCR1 expression group were markedly higher, and high SLNCR1 expression was correlated with greater susceptibility to LNM and DM. No relationship between SLNCR1 expression and tumor size and clinical features (age and gender) was observed. Moreover, the cutoff values varied among different studies, which might have caused heterogeneity in the results. In order to clarify the source of heterogeneity, we divided the comparison into subgroups with different cutoff values and analyzed the heterogeneity. As shown in Supplementary Materials (available here), the results showed no heterogeneity changes in OS, TNM, tumor size, LNM, DM, age, and gender. us, the cause of heterogeneity remains unclear.
Several limitations regarding this meta-analysis should be taken into account. Initially, the HRs and 95% CIs were extracted from Kaplan-Meier curves; lacking sufficient survival data may have led to extraneous heterogeneity. Second, all of the patients were from China, and the results may not represent the global population.
Furthermore, only 12 studies with 1155 patients were involved in the present meta-analysis. us, the small sample size of the study may have reduced the stringency of the conclusion. Finally, age and tumor size were defined by the ranges given in the included studies, and different studies had varying criteria for evaluating these parameters. us, more rigorous research studies are needed to confirm our conclusions.  Begg's funnel plot with pseudo 95% confidence limits In summary, high SLNCR1 expression was associated with poor OS, advanced tumor stage, LNM, and DM in multiple cancers.
us, the results of our meta-analysis indicate that SLNCR1 may serve as a prognosis biomarker for cancer patients in China.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.

Consent
Not applicable.

Conflicts of Interest
e authors declare that they have no conflicts of interest.