Aberrant expressed long non-coding RNAs in laryngeal squamous-cell carcinoma

Identification of diagnostic and prognostic lncRNA biomarkers in oral squamous carcinoma by integrated analysis and machine learning

BACKGROUND

Patients with oral squamous carcinoma (OSCC) present difficulty in precise diagnosis and poor prognosis.

OBJECTIVE

We aimed to identify the diagnostic and prognostic indicators in OSCC and provide basis for molecular mechanism investigation of OSCC.

METHODS

We collected sequencing data and clinical data from TCGA database and screened the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) in OSCC. Machine learning and modeling were performed to identify the optimal diagnostic markers. In order to determine lncRNAs with prognostic value, survival analysis was performed through combing the expression profiles with the clinical data. Finally, co-expressed DEmRNAs of lncRNAs were identified by interacted network construction and functional annotated by GO and KEGG analysis.

RESULTS

A total of 1114 (345 up- and 769 down-regulated) DEmRNAs and 156 (86 up- and 70 down-regulated) DElncRNAs were obtained in OSCC. Following the machine learning and modeling, 15 lncRNAs were identified to be the optimal diagnostic indicators of OSCC. Among them, FOXD2.AS1 was significantly associated with survival rate of patients with OSCC. In addition, Focal adhesion and ECM-receptor interaction pathways were found to be involved in OSCC.

CONCLUSIONS

FOXD2.AS1 might be a prognostic marker for OSCC and our study may provide more information to the further study in OSCC.

Screening key lncRNAs with diagnostic and prognostic value for head and neck squamous cell carcinoma based on machine learning and mRNA-lncRNA co-expression network analysis

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer around the world. The aim of this study was to seek the long non-coding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of HNSCC.

METHODS

Base on TCGA dataset, the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified between HNSCC and normal tissue. The machine learning and survival analysis were performed to estimate the potential diagnostic and prognostic value of lncRNAs for HNSCC. We also build the co-expression network and functional annotation. The expression of selected candidate mRNAs and lncRNAs were validated by Quantitative real time polymerase chain reaction (qRT-PCR).

RESULTS

A total of 3363 DEmRNAs (1822 down-regulated and 1541 up-regulated mRNAs) and 32 DElncRNAs (13 down-regulated and 19 up-regulated lncRNAs) between HNSCC and normal tissue were obtained. A total of 13 lncRNAs (IL12A.AS1, RP11.159F24.6, RP11.863P13.3, LINC00941, FOXCUT, RNF144A.AS1, RP11.218E20.3, HCG22, HAGLROS, LINC01615, RP11.351J23.1, AC024592.9 and MIR9.3HG) were defined as optimal diagnostic lncRNAs biomarkers for HNSCC. The area under curve (AUC) of the support vector machine (SVM) model, decision tree model and random forests model and were 0.983, 0.842 and 0.983, and the specificity and sensitivity of the three model were 95.5% and 96.2%, 77.3% and 97.6% and 93.2% and 97.8%, respectively. Among them, AC024592.9, LINC00941, LINC01615 and MIR9-3HG was not only an optimal diagnostic lncRNAs biomarkers, but also related to survival time. The focal adhesion, ECM-receptor interaction, pathways in cancer and cytokine-cytokine receptor interaction were four significantly enriched pathways in DEmRNAs co-expressed with the identified optimal diagnostic lncRNAs. But for most of the selected DEmRNAs and DElncRNAs, the expression was consistent with our integrated analysis results, including LINC00941, LINC01615, FOXCUT, TGA6 and MMP13.

CONCLUSION

AC024592.9, LINC00941, LINC01615 and MIR9-3HG was not only an optimal diagnostic lncRNAs biomarkers, but also were a prognostic lncRNAs biomarkers.