Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network

Linc00996 is a favorable prognostic factor in LUAD: Results from bioinformatics analysis and experimental validation

Background: Linc00996 has been reported in a variety of malignant tumors, but its potential role and significance in lung adenocarcinoma (LUAD) are not fully understood. The authors investigated the expression and biological behavior of Linc00996 in LUAD and elucidated the function of its potential target genes.

Materials and methods: The data of Linc00996 expression in cancers were derived from GEPIA. GEO and TCGA datasets were used to identify the differential expression of Linc00996 in LUAD and analyze the respective correlation between different expression levels and LUAD stage and survival prognosis. We further elucidated the potential biological processes and pathways involved with Linc00996 in LAUD by GSEA. ssGSEA was applied to explore the relationship between Linc00996 and immune activity. Finally, the clinical impact of Linc00996 was assessed in 61 patients with LUAD, and the biological functions of Linc00996 were determined by a series of experiments in vitro, such as CCK8, colony formation, migration, and invasion assays.

Results: Compared with adjacent normal lung tissues, Linc00996 was significantly downregulated in LUAD, and its expression was negatively correlated with T stage, N stage, and pathological stage. An in vitro study suggested that enhanced Linc00996 expression could inhibit cell proliferation, clonal formation, migration, and invasion in LUAD cell lines. Via GSEA and ssGSEA, we observed that Linc00996 might be connected with immune infiltration in LUAD, and Linc00996 might inhibit tumorigenesis and metastasis by regulating antigen processing and presentation, JAK-STAT3, and cell adhesion molecular signaling pathways.

Conclusion: Linc00996 is a novel tumor suppressor in LUAD and may suppress the tumorigenesis and metastasis of LUAD via the tumor-related signaling pathway, such as antigen processing and presentation, JAK-STAT3, and cell adhesion molecular signaling pathways.

A novel prognostic cancer-related lncRNA signature in papillary renal cell carcinoma

Background

Papillary renal cell carcinoma (pRCC) ranks second in renal cell carcinoma and the prognosis of pRCC remains poor. Here, we aimed to screen and identify a novel prognostic cancer-related lncRNA signature in pRCC.

Methods

The RNA-seq profile and clinical feature of pRCC cases were downloaded from TCGA database. Significant cancer-related lncRNAs were obtained from the Immlnc database. Differentially expressed cancer-related lncRNAs (DECRLs) in pRCC were screened for further analysis. Cox regression report was implemented to identify prognostic cancer-related lncRNAs and establish a prognostic risk model, and ROC curve analysis was used to evaluate its precision. The correlation between RP11-63A11.1 and clinical characteristics was further analyzed. Finally, the expression level and role of RP11-63A11.1 were studied in vitro.

Results

A total of 367 DECRLs were finally screened and 26 prognostic cancer-related lncRNAs were identified. Among them, ten lncRNAs (RP11-573D15.8, LINC01317, RNF144A-AS1, TFAP2A-AS1, LINC00702, GAS6-AS1, RP11-400K9.4, LUCAT1, RP11-63A11.1, and RP11-156L14.1) were independently associated with prognosis of pRCC. These ten lncRNAs were incorporated into a prognostic risk model. In accordance with the median value of the riskscore, pRCC cases were separated into high and low risk groups. Survival analysis indicated that there was a significant difference on overall survival (OS) rate between the two groups. The area under curve (AUC) in different years indicated that the model was of high efficiency in prognosis prediction. RP11-63A11.1 was mainly expressed in renal tissues and it correlated with the tumor stage, T, M, N classifications, OS, PFS, and DSS of pRCC patients. Consistent with the expression in pRCC tissue samples, RP11-63A11.1 was also down-regulated in pRCC cells. More importantly, up-regulation of RP11-63A11.1 attenuated cell survival and induced apoptosis.

Conclusions

Ten cancer-related lncRNAs were incorporated into a powerful model for prognosis evaluation. RP11-63A11.1 functioned as a cancer suppressor in pRCC and it might be a potential therapeutic target for treating pRCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02247-6.

Web tools to perform long non-coding RNAs analysis in oncology research

Accumulated evidence suggests that the widely expressed long-non-coding RNAs (lncRNAs) are involved in biogenesis. Some aberrant lncRNAs are closely related to pathological changes, for instance, in cancer. Both in tumorigenesis and cancer progression, depending on the interplay with cellular molecules, lncRNAs can modulate transcriptional interference, chromatin remodeling, post-translational regulation and protein modification, and further interfere with signaling pathways. Aiming to the diagnosis/ prognosis markers or potential therapeutical targets, it is important to figure out the specific mechanism and the tissue-specific expressing patterns of lncRNAs. Generally, the bioinformatics analysis is the first step. More and more in silico databases are increasing. But the existing integrative online platforms’ functions are not only having their unique features but also share some common features, which may lead to a waste of time for researchers. Here, we reviewed these web tools according to the functions. For each database, we clarified the data source, analysis method and the evidence that the analysis result is derived from. This review also illustrated examples in practical use for a specific lncRNA by these web tools. It will provide convenience for researchers to quickly choose the appropriate bioinformatics web tools in oncology studies.