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Identification of Novel Transcription Factor-Microrna-Mrna Co-Regulatory Networks in Pulmonary Large-Cell Neuroendocrine Carcinoma

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Identification of Novel Transcription Factor-Microrna-Mrna Co-Regulatory Networks in Pulmonary Large-Cell Neuroendocrine Carcinoma 133 Original Article Page 1 of 15 Identification of novel transcription factor-microRNA-mRNA co-regulatory networks in pulmonary large-cell neuroendocrine carcinoma Cunliang Cai1^, Qianli Zeng2, Guiliang Zhou2, Xiangdong Mu1 1Department of Respiratory and Critical Care Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China; 2The South China Center for Innovative Pharmaceuticals, Guangzhou, China Contributions: (I) Conception and design: C Cai, X Mu; (II) Administrative support: X Mu; (III) Provision of study materials or patients: C Cai; (IV) Collection and assembly of data: Q Zeng, G Zhou; (V) Data analysis and interpretation: Q Zeng, G Zhou; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. Correspondence to: Dr. Xiangdong Mu. Department of Respiratory and Critical Care Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China. Email: [email protected]. Background: Large cell neuroendocrine carcinoma (LCNEC) of the lung is a rare neuroendocrine neoplasm. Previous studies have shown that microRNAs (miRNAs) are widely involved in tumor regulation through targeting critical genes. However, it is unclear which miRNAs play vital roles in the pathogenesis of LCNEC, and how they interact with transcription factors (TFs) to regulate cancer-related genes. Methods: To determine the novel TF-miRNA-target gene feed-forward loop (FFL) model of LCNEC, we integrated multi-omics data from Gene Expression Omnibus (GEO), Transcriptional Regulatory Relationships Unraveled by Sentence-Based Text Mining (TRRUST), Transcriptional Regulatory Element Database (TRED), and The experimentally validated microRNA-target interactions database (miRTarBase database). First, expression profile datasets for mRNAs (GSE1037) and miRNAs (GSE19945) were downloaded from the GEO database. Overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified through integrative analysis. The target genes of the FFL were obtained from the miRTarBase database, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed on the target genes. Then, we screened for key miRNAs in the FFL and performed gene regulatory network analysis based on key miRNAs. Finally, the TF-miRNA-target gene FFLs were constructed by the hypergeometric test. Results: A total of 343 DEGs and 60 DEMs were identified in LCNEC tissues compared to normal tissues, including 210 down-regulated and 133 up-regulated genes, and 29 down-regulated and 31 up- regulated miRNAs. Finally, the regulatory network of TF-miRNA-target gene was established. The key regulatory network modules included ETS1-miR195-CD36, TAOK1-miR7-1-3P-GRIA1, E2F3-miR195- CD36, and TEAD1-miR30A-CTHRC1. Conclusions: We constructed the TF-miRNA-target gene regulatory network, which is helpful for understanding the complex LCNEC regulatory mechanisms. Keywords: Large cell neuroendocrine carcinoma (LCNEC); microRNA (miRNA); feed-forward loop (FFL); transcription factor-miRNA co-regulatory network (TF-miRNA co-regulatory network) Submitted Nov 03, 2020. Accepted for publication Jan 06, 2021. doi: 10.21037/atm-20-7759 View this article at: http://dx.doi.org/10.21037/atm-20-7759 ^ ORCID: 0000-0003-4934-2493. © Annals of Translational Medicine. All rights reserved. Ann Transl Med 2021;9(2):133 | http://dx.doi.org/10.21037/atm-20-7759 Page 2 of 15 Cai et al. The miRNA-mRNA co-regulatory networks in LCNEC Introduction the miR-381-YAP-Snail signal axis might be a suitable diagnostic marker and a potential therapeutic target for lung Pulmonary high-grade neuroendocrine carcinoma includes cancer (13). Foxp3 has been reported as a key regulatory the following 2 clinicopathological entities classified based gene of regulatory T cells (Tregs). Foxp3+ Tregs accumulate on their morphological and biological features: large cell in the center of lung tumors and in metastatic lymph nodes, neuroendocrine carcinoma (LCNEC) and small cell lung suggesting a role in the formation of immunosuppressive cancer (SCLC) (1,2). LCNEC and SCLC share several tumor microenvironment (14). Shih et al. validated that histological features, including rosette formation, molding MYC, YAP1, or MMP13 overexpression increased the of nuclei, and lack of apparent glandular formation and incidence of lung adenocarcinoma brain metastasis by case- keratinization (3,4). LCNEC, diagnosed in approximately controlled analyses of genomic alterations and functional 3% of all patients with lung cancer, is generally associated assessment in patient-derived xenograft mouse models (15). with a high metastasis rate and poor prognosis (5,6). It is MicroRNAs (miRNAs) are short, non-coding RNAs characterized by occult onset, strong invasiveness, and poor consisting of 18-25 nucleotides that regulate the translation survival regardless of surgery, chemotherapy, radiotherapy, of mRNAs (16). Mature miRNAs can recognize and bind or other treatments (7). For the treatment of LCNEC, to the 3' untranslated region of mRNAs and regulate surgical resection remains the only curative treatment translation at the post-transcriptional level through for early-stage patients. For advanced-stage LCNECs, repression or degradation of the targeted mRNAs (17). the standard treatment is debated. Some clinicians favor Recent studies have documented the relationship between chemotherapy similar to SCLC (platinum-etoposide) the aberrant expression of a class of miRNAs and the while others prefer NSCLC-type chemotherapy regimens, pathogenesis of many human cancers, including lung like gemcitabine/pemetrexed combined with platinum. cancer (18,19). Lee et al. found that miR-21 and miR-155 Therefore, because there is a paucity in the understanding were differentially expressed according to the histological of the precise molecular mechanisms underlying LCNEC, subtypes of pulmonary neuroendocrine tumors, and the it is of great importance to find biological markers for early expression level of miR-21 was significantly higher in diagnosis and novel treatment targets of LCNEC. carcinoid tumors with lymph node metastasis than in Transcription factors (TFs) are DNA-binding proteins carcinoid tumors without lymph node metastasis (20). that can function as tumor suppressors or oncogenes (8). Other links between lung cancer and miRNA have also TFs play significant roles in the regulation of gene been reported, including let-7 in lung cancers (21), and high expression, and can induce avoidance of apoptosis and expression and oncogenic function of mir-17-92 cluster in uncontrolled growth (9). A previous study demonstrated human B cell lymphomas as well as in lung cancers (22). the presence of thyroid transcription factor-1 (TTF-1) in The expression of 5 miRNAs (hsa-mir-155, hsamir-17-3p, a significant subset of pulmonary neuroendocrine tumors, hsa-let-7a-2, hsa-mir-145, and hsa-mir-21) has been shown including 35% of typical carcinoids, 100% of atypical to be significantly altered in lung cancers, and also has a carcinoids, 75% of large cell neuroendocrine tumors, and prognostic impact on survival (18). 95% of small cell carcinomas (10). Huang et al. found that Regulatory network analysis, such as feedback loop and POU2F3 is an essential master regulator of cell identity feed-forward loop (FFL), is a powerful way to investigate in the neuroendocrine variant of SCLC. Epithelial- the underlying global relationships between molecular mesenchymal transition (EMT) plays an important role networks. MiRNA-TF co-regulation is one of the most in tumor invasion and metastasis, and is an important important FFL types. TFs and miRNAs are crucial mechanism by which many tumor cell types acquire invasion regulators at the transcriptional and post-transcriptional and metastasis capabilities (11). Snail, a major TF regulator levels. Understanding the cross-talk between these 2 of EMT, directly inhibits the expression of E-cadherin regulators and their targets is critical to unveiling the (E-cad) and up-regulates the expression of interstitial cell complex molecular regulatory mechanisms of cancers. marker molecules such as N-cadherin (N-cad), thereby Numerous studies have revealed that TFs regulate gene promoting the oncogenesis of non-small cell lung cancer expression by interacting with miRNAs (23). There are (NSCLC) (12). Jin et al. found that metformin-induced currently no detailed reports of the TFs and TF-miRNA- repression of miR-381-YAP-Snail axis activity can disrupt mRNA network in LCNEC. NSCLC growth and metastasis. Thus, they postulated that Recently, by identifying key cancer-related genes, non- © Annals of Translational Medicine. All rights reserved. Ann Transl Med 2021;9(2):133 | http://dx.doi.org/10.21037/atm-20-7759 Annals of Translational Medicine, Vol 9, No 2 January 2021 Page 3 of 15 coding RNAs, and TFs, bioinformatics analysis has gradually limma 10.0 package in Bioconductor version 3.10 (http:// been utilized to explore the mechanisms of oncogenesis www.bioconductor.org/) was utilized to identify DEGs and and cancer progression. In this study, we investigated aberrantly expressed miRNAs in LCNEC tissues compared the comprehensive miRNA-TF co-regulatory network with normal lung tissues. The Benjamini and Hochberg in LCNEC. Firstly, we identified the potential targets false discovery rate (FDR) and adjusted P values (adj.p) were of LCNEC-related TFs and miRNAs. We used a
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