Thyroid cancer is a rare malignancy and accounts for less than 1% of malignant neoplasms in humans; however, it is the most common cancer of the endocrine system and responsible for most deaths from endocrine cancer. Long non-coding (Lnc)RNAs are defined as non-coding transcripts that are more than 200 nucleotides in length. Their expression deregulation plays an important role in the progress of cancer. These molecules are involved in physiologic cellular processes, genomic imprinting, inactivation of chromosome X, maintenance of pluripotency, and the formation of different organs via changes in chromatin, transcription, and translation. LncRNAs can act as a tumor suppressor genes or oncogenes. Several studies have shown that these molecules can interact with microRNAs and prevent their binding to messenger RNAs. Research has shown that these molecules play an important role in tumorigenicity, angiogenesis, proliferation, migration, apoptosis, and differentiation. In thyroid cancer, several lncRNAs (MALAT1, H19, BANCR, HOTAIR) have been identified as contributing factors to cancer development, and can be used as novel biomarkers for early diagnosis or even treatment. In this article, we study the newest lncRNAs and their role in thyroid cancer.
Background
A deep understanding of potential molecular biomarkers and therapeutic targets related to the progression of colorectal cancer (CRC) from early stages to metastasis remain mostly undone. Moreover, the regulation and crosstalk among different cancer-driving molecules including messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs) and micro-RNAs (miRNAs) in the transition from stage I to stage IV remain to be clarified, which is the aim of this study.
Methods
We carried out two separate differential expression analyses for two different sets of samples (stage-specific samples and tumor/normal samples). Then, by the means of robust dataset analysis we identified distinct lists of differently expressed genes (DEGs) for Robust Rank Aggregation (RRA) and weighted gene co-expression network analysis (WGCNA). Then, comprehensive computational systems biology analyses including mRNA-miRNA-lncRNA regulatory network, survival analysis and machine learning algorithms were also employed to achieve the aim of this study. Finally, we used clinical samples to carry out validation of a potential and novel target in CRC.
Results
We have identified the most significant stage-specific DEGs by combining distinct results from RRA and WGCNA. After finding stage-specific DEGs, a total number of 37 DEGs were identified to be conserved across all stages of CRC (conserved DEGs). We also found DE-miRNAs and DE-lncRNAs highly associated to these conserved DEGs. Our systems biology approach led to the identification of several potential therapeutic targets, predictive and prognostic biomarkers, of which lncRNA LINC00974 shown as an important and novel biomarker.
Conclusions
Findings of the present study provide new insight into CRC pathogenesis across all stages, and suggests future assessment of the functional role of lncRNA LINC00974 in the development of CRC.
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