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Background The accuracy of existing biomarkers for predicting the prognosis of hepatocellular carcinoma (HCC) is not satisfactory. It is necessary to explore biomarkers that can accurately predict the prognosis of HCC. Methods In this study, original transcriptome data were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related long noncoding ribonucleic acids (irlncRNAs) were identified by coexpression analysis, and differentially expressed irlncRNA (DEirlncRNA) pairs were distinguished by univariate analysis. In addition, the least absolute shrinkage and selection operator (LASSO) penalized regression was modified. Next, the cutoff point was determined based on the area under the curve (AUC) and Akaike information criterion (AIC) values of the 5-year receiver operating characteristic (ROC) curve to establish an optimal model for identifying high-risk and low-risk groups of HCC patients. The model was then reassessed in terms of clinicopathological features, survival rate, tumor-infiltrating immune cells, immunosuppressive markers, and chemotherapy efficacy. Results A total of 1009 pairs of DEirlncRNAs were recognized in this study, 30 of these pairs were included in the Cox regression model for subsequent analysis. After regrouping according to the cutoff point, we could more effectively identify factors such as aggressive clinicopathological features, poor survival outcomes, specific immune cell infiltration status of tumors, high expression level of immunosuppressive biomarkers, and low sensitivity to chemotherapy drugs in HCC patients. Conclusions The nonspecific expression level signature involved with irlncRNAs shows promising clinical value in predicting the prognosis of HCC patients.

Background The snoRNA host gene SNHG15 produces a long non-coding RNA (lncRNA) with a short half-life and has been reported to be dysregulated in multiple cancers and has recently been found to be correlated with tumour progression. Therefore, this meta-analysis was performed to evaluate the generalised prognostic role of small nucleolar RNA host gene 15 (SNHG15) in malignancies, based on variable data from different studies. Methods Four public databases were used to identify eligible studies. The association between prognostic indicators and clinical features was extracted and pooled to estimate the hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs). Publication bias was measured using Begg’s test and Egger’s test, and the stability of pooled results were measured using sensitivity analysis. Additionally, an online database based on The Cancer Genome Atlas (TCGA) was screened to further validate our results. Ultimately, we predicted the molecular regulation of SNHG15 based on the public databases. Results In total, 11 studies including 1087 patients were ultimately enrolled in our meta-analysis. We found that SNHG15 overexpression was associated with worse overall survival (OS) and disease-free survival (DFS), and this was validated in the Gene Expression Profiling Interactive Analysis (GEPIA) cohort. Moreover, increased SNHG15 expression suggested advanced TNM stage and LNM, but was not associated with age, gender, or tumour size. No publication bias or instability of the results was observed. SNHG15 was significantly upregulated in seven cancers and elevated expression of SNHG15 indicated shorter OS and DFS in five malignancies based on the validation using the GEPIA cohort. Further functional prediction indicated that SNHG15 may participate in some cancer-related pathways. Conclusions Upregulation of lncRNA SNHG15 was notably associated with worse prognosis and clinical features, suggesting that SNHG15 might serve as a novel prognostic factor in various cancers.

Background Long non-coding RNA P73 antisense RNA 1 T (non-protein coding), also known as Lnc RNA TP73-AS1, is dysregulated in various tumors but the correlation between its expression and clinicopathological parameters and/or prognoses in cancer patients is inconclusive. Here, we performed a meta-analysis to evaluate the prognostic value of Lnc RNA TP73-AS1 for malignancies. Methods We systematically searched four online databases including PubMed, the Web of Science, Embase, and the Cochrane Library for eligible articles published up to June 29/2020. Odds ratios (ORs) and Pooled hazard ratios (HRs) with 95% confidence intervals (95% CIs) were used to assess the association of TP73-AS1 expression with prognostic and clinicopathological parameters. We further validated TP73-AS1 expression in various malignancies and its potential prognostic value using the GEPIA online database. We predicted potential biological processes and relevant signal mechanisms through the public databases. Results A total of 26 studies examining 14 cancers were analyzed to evaluate the relationship between TP73-AS1 expression, clinicopathological features and prognostic indicators. The results indicated that TP73-AS1 expression markedly correlates with TNM stage (OR = 3.27,95% CI:2.43–4.39, P P P 62–7.73,P P > 0.05), gender (OR = 1.08, 95%CI:0.84–1.38, P > 0.05) or differentiation (OR = 1.39, 95%CI:0.71–2.70, P = 0.340) was observed. TP73-AS1 overexpression was a biomarker of poor Overall survival(OS)(HR = 1.85,95%CI:1.53–2.22, P P Conclusions The upregulation of Lnc RNA TP73-AS1 was related to detrimental clinicopathological parameters and can be considered an indicator of poor prognosis for cancer malignancies.