Comprehensive Analysis of Novel lncRNA-TF Regulatory Cross Talks and Identification of Core lncRNA-TF Feedback Loops in Sarcoma

Construction and Characterization of Long Non-Coding RNA-Associated Networks to Reveal Potential Prognostic Biomarkers in Human Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one type of the malignant tumors with high morbidity and mortality. The molecular mechanism of LUAD is still unclear. Studies demonstrate that lncRNAs play crucial roles in LUAD tumorigenesis and can be used as prognosis biomarkers. Thus, in this study, to identify more robust biomarkers of LUAD, we firstly constructed LUAD-related lncRNA-TF network and performed topological analyses for the network. Results showed that the network was a scale-free network, and some hub genes with high clinical values were identified, such as lncRNA RP11-173A16 and TF ZBTB37. Module analysis on the network revealed one close lncRNA module, which had good prognosis performance in LUAD. Furthermore, through integrating ceRNAs strategy and TF regulatory information, we identified some lncRNA-TF positive feedback loops. Prognostic analysis revealed that ELK4- and BDP1-related feedback loops were significant. Secondly, we constructed the lncRNA-m6A regulator network by merging all the high correlated lncRNA-m6A regulator pairs. Based on the network analysis results, some key m6A-related lncRNAs were identified, such as MIR497HG, FENDRR, and RP1-199J3. We also investigated the relationships between these lncRNAs and immune cell infiltration. Results showed that these m6A-related lncRNAs were high correlated with tumor immunity. All these results provide a new perspective for the diagnostic biomarker and therapeutic target identification of LUAD.

Integrated analysis of immune-related genes in endometrial carcinoma

Background

Exploring novel and sensitive targets is urgent due to the high morbidity of endometrial cancer (EC). The purpose of our study was to explore the transcription factors and immune-related genes in EC and further identify immune-based lncRNA signature as biomarker for predicting survival prognosis.

Methods

Transcription factors, aberrantly expressed immune-related genes and immune-related lncRNAs were explored through bioinformatics analysis. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were conducted to identify the immune and overall survival (OS) related lncRNAs. The accuracy of model was evaluated by Kaplan-Meier method and receiver operating characteristic (ROC) analysis, and the independent prognostic indicator was identified with Cox analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) were conducted to detect the accuracy of our results.

Results

A network of 29 transcription factors and 17 immune-related genes was constructed. Furthermore, four immune-prognosis-related lncRNAs were screened out. Kaplan-Meier survival analysis and time-dependent ROC analysis revealed a satisfactory predictive potential of the 4-lncRNA model. Consistency was achieved among the results from the training set, testing set and entire cohort. The distributed patterns between the high- and low-risk groups could be distinguished in principal component analysis. Comparisons of the risk score and clinical factors confirmed the four-lncRNA-based signature as an independent prognostic indicator. Last, the reliability of the results was verified by qRT-PCR in 29 cases of endometrial carcinoma and in cells.

Conclusions

Overall, our study constructed a network of transcription factors and immune-related genes and explored a four immune-related lncRNA signature that could serve as a novel potential biomarker of EC.