Comprehensive analysis of long noncoding RNA–mRNA co-expression patterns in thyroid cancer

Inferring cell developmental stage-specific lncRNA regulation in the developing human neocortex with CDSlncR

Noncoding RNAs (ncRNAs) occupy ~98% of the transcriptome in human, and are usually not translated into proteins. Among ncRNAs, long non-coding RNAs (lncRNAs, >200 nucleotides) are important regulators to modulate gene expression, and are involved in many biological processes (e.g., cell development). To study lncRNA regulation, many computational approaches or tools have been proposed by using bulk transcriptomics data. Nevertheless, previous bulk data-driven methods are mostly limited to explore the lncRNA regulation regarding all of cells, instead of the lncRNA regulation specific to cell developmental stages. Fortunately, recent advance in single-cell sequencing data has provided a way to investigate cell developmental stage-specific lncRNA regulation. In this work, we present a novel computational method, CDSlncR (Cell Developmental Stage-specific lncRNA regulation), which combines putative lncRNA-target binding information with single-cell transcriptomics data to infer cell developmental stage-specific lncRNA regulation. For each cell developmental stage, CDSlncR constructs a cell developmental stage-specific lncRNA regulatory network in the cell developmental stage. To illustrate the effectiveness of CDSlncR, we apply CDSlncR into single-cell transcriptomics data of the developing human neocortex for exploring lncRNA regulation across different human neocortex developmental stages. Network analysis shows that the lncRNA regulation is unique in each developmental stage of human neocortex. As a case study, we also perform particular analysis on the cell developmental stage-specific lncRNA regulation related to 18 known lncRNA biomarkers in autism spectrum disorder. Finally, the comparison result indicates that CDSlncR is an effective method for predicting cell developmental stage-specific lncRNA targets. CDSlncR is available at https://github.com/linxi159/CDSlncR.

Long Non-Coding RNAs Profiling Using Microarray in Papillary Thyroid Carcinoma

Long non-coding RNAs (lncRNAs) have been implicated in various cancers, including papillary thyroid carcinomas (PTCs). Genome-wide analysis (GWAS) of lncRNAs expression in PTC samples exhibited up and down regulation of lncRNAs, thus, acting as tumor promoting oncogenes or tumor suppressors in the pathogenesis of PTC by interacting with target genes. For example, lncRNAs such as HOTAIR, NEAT1, MALAT1, FAL1, HOXD-AS1, etc. are overexpressed in PTC in comparison to that of non-cancerous thyroid tissues, which stimulate the pathogenesis of PTC. On the other hand, lncRNAs such as MEG3, CASC2, PANDAR, LINC00271, NAMA, PTCSC3, etc. are down regulated in PTC tissues when compared to that of non-cancerous thyroid samples, suppressing formation of PTC. Also, several lncRNAs such as BANCR acts as oncogenic or tumor suppressor in PTC formation depending on which they are interacting with. In addition, lncRNAs expression in patients with PTC associated with clinicopathological parameters such as distance metastasis, lymph node metastasis, tumor size, pathological stage, and response to therapy. Thus, lncRNAs profiles could have the potential to be used as prognostic or predictive biomarker in patients with PTC. Therefore, we describe the microarray method to examine lncRNAs expression in PTC tissue samples, which could facilitate better management of patients with PTC. Furthermore, this method could be fabricated to examine lncRNAs expression in other biological and/or clinical samples.

A ceRNA network mediated by LINC00475 in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most frequent histological type of differentiated thyroid carcinoma. Long noncoding RNAs (lncRNAs) have been widely reported to play a key role in human malignancies, and PTC is included. This study aimed to find out the functions and mechanism of lncRNA LINC00475 in PTC. LINC00475 was upregulated in PTC cells and was mainly located in the cytoplasm according to reverse-transcription polymerase chain reaction analyses and subcellular fractionation assays. As shown by cell counting kit-8 assays, ethynyl deoxyuridine incorporation assays, wound healing assays, and transwell assays, LINC00475 knockdown suppressed cell viability, proliferation, migration, and invasion. Mechanistically, LINC00475 upregulated the expression of messenger RNA zinc finger CCHC-type containing 12 (ZCCHC12) by binding to miR-376c-3p. ZCCHC12 was a direct target gene of miR-376c-3p in PTC cells. The relationship between miR-376c-3p and LINC00475 (or ZCCHC12) in PTC cells was probed by luciferase reporter assays, RNA pulldown assays, and RNA immunoprecipitation assays. In addition, both mRNA and protein levels of ZCCHC12 were downregulated due to miR-376c-3p overexpression or LINC00475 silencing. ZCCHC12 overexpression partially reversed the suppressive effect of LINC00475 knockdown on malignant behaviors of PTC cells. In conclusion, LINC00475 promotes PTC cell proliferation, migration, and invasion by upregulating ZCCHC12 via the interaction with miR-376c-3p.

Whole‑genome identification and systematic analysis of lncRNA‑mRNA co‑expression profiles in patients with cutaneous basal cell carcinoma

Cutaneous basal cell carcinoma (BCC) is a common subtype of malignant skin tumor with low invasiveness. Early diagnosis and treatment of BCC and the identification of specific biomarkers are particularly urgent. Long non‑coding RNAs (lncRNAs) have been shown to be associated with the development of various tumors, including BCC. The present study conducted a comparative analysis of the differential expression of lncRNAs and mRNAs through whole‑genome technology. Microarray analyses were used to identify differentially expressed (DE) lncRNAs and DE mRNAs. Reverse transcription‑quantitative (RT‑q) PCR confirmed the differential expression of 10 lncRNAs in BCC. Subsequently, a lncRNA‑mRNA co‑expression network was constructed using the top 10 DE lncRNAs. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the possible biological effects of the identified mRNAs and to speculate on the possible biological effects of the lncRNAs. A total of 1,838 DE lncRNAs and 2,010 DE mRNAs were identified and 10 of the DE lncRNAs were confirmed by RT‑qPCR. A lncRNA‑mRNA co‑expression network comprising 166 specific co‑expressed lncRNAs and mRNAs was constructed using the top 10 DE lncRNAs. According to the results of the GO and KEGG analyses, lncRNA XR_428612.1 may serve an important role in mitochondrial dysfunction and the progression of BCC by modulating TICAM1, USMG5, COX7A2, FBXO10, ATP5E and TIMM8B. The present study provided whole‑genome identification and a systematic analysis of lncRNA‑mRNA co‑expression profiles in BCC.

Long non-coding RNA DIO3OS/let-7d/NF-κB2 axis regulates cells proliferation and metastasis of thyroid cancer cells

Due to the steadily rising morbidity and mortality, thyroid cancer remains the most commonly seen endocrine cancer. The present study attempted to investigate the mechanism from the perspective of long non-coding RNA (lncRNA) regulation. We identified 53 markedly increased lncRNAs in thyroid cancer samples according to TCGA data. Among them, high lncRNA DIO3OS expression was a risk factor for thyroid cancer patients' poorer overall survival. DIO3OS showed to be considerably increased within thyroid cancer tissue samples and cells. Knocking down DIO3OS within thyroid carcinoma cells suppressed cancer cell viability, the capacity of DNA synthesis, cell invasion, as well as cell migration; besides, proliferating markers, ki-67 and PCNA, were decreased by DIO3OS knockdown. Cancer bioinformatics analysis suggested that NF-κB2 might be related to DIO3OS function in thyroid cancer carcinogenesis. NF-κB2 was positively correlated with DIO3OS, and DIO3OS knockdown decreased NF-κB2 protein levels. Knocking down NF-κB2 within thyroid carcinoma cells suppressed cancer cell viability, the capacity of DNA synthesis, cell invasion, cell migration, and the protein levels of proliferating markers. Let-7d directly targeted DIO3OS and NF-κB2; DIO3OS knockdown upregulated let-7d expression. The overexpression of let-7d suppressed cancer cell viability, the capacity of DNA synthesis, cell invasion, cell migration, as well as the protein levels of proliferating markers. Let-7d inhibition remarkably attenuated the functions of DIO3OS knockdown in NF-κB2 expression and thyroid cancer cell phenotype. In conclusion, DIO3OS/let-7d/NF-κB2 axis regulates the viability, DNA synthesis capacity, invasion, and migration of thyroid cancer cells. The clinical application of this axis needs further in vivo and clinical investigation.

Inferring and analyzing module-specific lncRNA-mRNA causal regulatory networks in human cancer

It is known that noncoding RNAs (ncRNAs) cover ∼98% of the transcriptome, but do not encode proteins. Among ncRNAs, long noncoding RNAs (lncRNAs) are a large and diverse class of RNA molecules, and are thought to be a gold mine of potential oncogenes, anti-oncogenes and new biomarkers. Although only a minority of lncRNAs is functionally characterized, it is clear that they are important regulators to modulate gene expression and involve in many biological functions. To reveal the functions and regulatory mechanisms of lncRNAs, it is vital to understand how lncRNAs regulate their target genes for implementing specific biological functions. In this article, we review the computational methods for inferring lncRNA-mRNA interactions and the third-party databases of storing lncRNA-mRNA regulatory relationships. We have found that the existing methods are based on statistical correlations between the gene expression levels of lncRNAs and mRNAs, and may not reveal gene regulatory relationships which are causal relationships. Moreover, these methods do not consider the modularity of lncRNA-mRNA regulatory networks, and thus, the networks identified are not module-specific. To address the above two issues, we propose a novel method, MSLCRN, to infer and analyze module-specific lncRNA-mRNA causal regulatory networks. We have applied it into glioblastoma multiforme, lung squamous cell carcinoma, ovarian cancer and prostate cancer, respectively. The experimental results show that MSLCRN, as an expression-based method, could be a useful complementary method to study lncRNA regulations.

Influencers on Thyroid Cancer Onset: Molecular Genetic Basis

Thyroid cancer, a cancerous tumor or growth located within the thyroid gland, is the most common endocrine cancer. It is one of the few cancers whereby incidence rates have increased in recent years. It occurs in all age groups, from children through to seniors. Most studies are focused on dissecting its genetic basis, since our current knowledge of the genetic background of the different forms of thyroid cancer is far from complete, which poses a challenge for diagnosis and prognosis of the disease. In this review, we describe prevailing advances and update our understanding of the molecular genetics of thyroid cancer, focusing on the main genes related with the pathology, including the different noncoding RNAs associated with the disease.

Long noncoding RNAs in thyroid cancer

PURPOSE OF REVIEW

Our understanding of the molecular pathology events involved in thyroid cancer initiation and progression and its subtypes has markedly improved as a result of multiomic studies. Recently, long noncoding RNA (lncRNA) have been shown to have a role in cancer initiation and progression and have also been studied in thyroid cancer.

RECENT FINDINGS

lncRNA are dysregulated in thyroid cancer. lncRNA have tumor suppressive and oncogenic function in thyroid cancer cells and play a role in some of the established genetic drivers of thyroid cancer initiation and progression. Lastly, some lncRNA are associated with clinicopathologic features of thyroid cancer and circulating blood lncRNA could potentially detect the presence of thyroid cancer.

SUMMARY

We highlight the possible clinical utility of analyzing lncRNAs as biomarkers for thyroid cancer diagnosis and prognosis and their association with common genetic changes associated with thyroid cancer.

Role of a novel circulatory RNA-based biomarker panel expression in ovarian cancer

Ovarian cancer (OC) is considered the sixth commonest cancer affecting women globally. We choose novel integrated specific ovarian cancer RNA biomarker panel; pellino E3 ubiquitin protein ligase family member 3 (PELI3) gene expressions along with its selected epigenetic regulators (microRNA (miR-361-3p) and long noncoding RNA (lncRNA RP5-837J1.2) by bioinformatic methods. Then, differential expressions of the selected panel in the sera of 50 OC patients, 42 cases with benign ovarian lesions, and among 45 controls were determined using real-time polymerase chain reaction quantitative (qRT-PCR). Furthermore, their expression was measured also in malignant ovarian tissues and adjacent nontumor tissues in 23 of 50 OC patients by quantitative qRT-PCR. The current study reported, for the first time, upregulation of serum lncRNA RP5-837J1.2 with concomitant downregulation of miR-361-3p and PELI3 mRNA in malignant group compared with benign and controls groups. There were associations of serum lncRNA RP5-837J1.2 with the affected ovary and worse International Federation of Gynecology and Obstetrics staging; associations of miR-361-3p with tumor size, grade, stage, and presence of metastasis; as well as associations among PELI3 mRNA expression and tumor size, grade, stage, and presence of metastasis among the OC group. In tumor tissues, miR-361-3p and PELI3 mRNA levels were at a higher level than that of nontumor tissues; however, tumor tissue showed lower level of lncRNA RP5-837J1.2 compared to normal tissue. There were positive correlations between serum and tissue level of RNA RP5-837J1.2, miR-361-3p, and PELI3 mRNA, but they did not reach statistical significance. Receiver operating characteristics curve analyses showed that lncRNA RP5-837J1.2, miR-361-3p, and PELI3 mRNA expression levels can discriminate among OC patient, cases with benign mass, and controls with an accuracy of 96, 76, and 83%, respectively; which increased if they are combined. This novel diagnostic RNA-based panel biomarker could be helpful for OC diagnosis.

Co-expression network analysis of the lncRNAs and mRNAs associated with cervical cancer progression

INTRODUCTION

Cervical cancer is the second most common type of cancer and the third leading cause of cancer deaths in females in developing countries. Recent studies showed that long non-coding RNAs play a key role in human cancers. However, the molecular mechanisms underlying the initiation and progression of cervical cancer remained to be further explored.

MATERIAL AND METHODS

In this study, we explored the differential expression of lncRNAs and mRNAs in cervical cancer progression by analyzing the public dataset GSE63514. Next, PPI and co-expression networks were constructed to reveal the potential roles of cervical cancer related mRNAs and lncRNAs. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed to explore functions of differentially expressed genes (DEGs) in cervical cancer.

RESULTS

In the present study, we observed that 3021 mRNAs were up-regulated and 1605 mRNAs were down-regulated in cervical cancer progression. Meanwhile, we for the first time found that 172 lncRNAs were up-regulated and 106 lncRNAs were down-regulated in cervical cancer progression. Co-expression network analysis showed that lncRNAs were widely co-expressed with cell cycle related genes in cervical cancer, implicating the important roles of these lncRNAs in cell proliferation regulation. Of note, we identified two hub lncRNA-mRNA networks involved in regulating various biological processes in cervical cancer progression.

CONCLUSIONS

Our results identified key mRNAs and lncRNAs in cervical cancer progression. This study will provide novel insights to explore the potential mechanisms underlying cervical cancer progression.

Integrated analysis of long noncoding RNA interactions reveals the potential role in progression of human papillary thyroid cancer

Recent scientific evidence has suggested that long noncoding RNAs (lncRNAs) play an important part in tumorigenesis as an important member of competing endogenous RNAs (ceRNAs). Hundreds of RNA sequence data and relevant clinic information are freely accessible in The Cancer Genome Atlas (TCGA) datasets. However, the role of cancer‐related lncRNAs in papillary thyroid cancer (PTC) is not fully understood yet. In this study, we identified 461 RNA sequencing data from TCGA. Subsequently, 45 lncRNAs, 21 miRNAs, and 78 mRNAs were chosen to construct a ceRNA network of PTC. Then, we analyzed the correlation between these 45 PTC‐specific lncRNAs and clinic features and patient outcome. Thirty‐seven of these lncRNAs were found to be closely related to age, race, gender, lymph node metastasis, TNM staging system, and patient outcome. Additionally, three of them were linked to PTC patient overall survival. Eventually, we selected eight lncRNAs randomly and performed quantificational real‐time polymerase chain reaction (qRT‐PCR) in 28 newly diagnosed patients with PTC to verify the reliability of the above results. The results of qRT‐PCR are totally in agreement with the bioinformatics analysis. Additionally, it was found that HAND2‐AS1 was negatively related to tumor size (P < 0.05). The results were consistent with the bioinformatics analysis in TCGA. Taken together, we identified the differentially expressed lncRNAs and constructed a PTC ceRNA network. The study provides a new perspective and supplement for our understanding of lncRNAs in PTC development and reveals potential diagnostic and prognostic markers in PTC.

Screening key long non-coding RNAs in early-stage colon adenocarcinoma by RNA-sequencing

AIM

We aim to identify the key long noncoding RNAs (lncRNAs) in early-stage colon adenocarcinoma (COAD).

PATIENTS & METHODS

Compared with colonic intraepithelial neoplasia, differentially expressed lncRNAs (DElncRNAs) in early-stage COAD were obtained by RNA-sequencing. Our previous work has obtained the differentially expressed mRNAs and miRNAs (DEmRNAs and DEmiRNAs) in early-stage COAD. DEmiRNA-DElncRNA-DEmRNA interaction analysis and functional annotation were performed. Validation of expression and receiver-operating characteristic analyses were performed based on The Cancer Genome Atlas.

RESULTS

Seventy-nine significantly DElncRNAs in early-stage COAD were obtained. MiR-153-3p-TUG1-DAPK1/ARNT2/KLK3/PLD1/SMAD2 and miR-153-3p-SNHG17-COL11A1/IGFBP3/KLF6 interactions were associated with early-stage COAD. Five DElncRNAs (ELFN1-AS1, LINC01234, SNHG17, UCA1 and LOC101929549) involved in early-stage COAD with potential diagnostic value.

CONCLUSION

LncRNAs involve in early-stage COAD by interaction with COAD-regulated genes and miRNAs.

Knockdown of long non-coding RNA HOXD-AS1 inhibits the progression of osteosarcoma

Long non-coding RNA HOXD-AS1 (HOXD-AS1) has recently been shown to be involved in the development and progression of multiple cancers. However, the expression, significance, and biological function of HOXD-AS1 in osteosarcoma (OS) remain unknown. Here, we found that the expression level of HOXD-AS1 was significantly upregulated in OS tissues and cells. Furthermore, high expression of HOXD-AS1 was positively associated with the clinical and pathological characteristics of OS, including tumor stage and lymph node metastasis, and negatively correlated with overall survival rate. in vitro assays confirmed that knockdown of HOXD-AS1 suppressed cell proliferation, colony formation, migration, and invasion, and promoted cell cycle arrest at G1 stage and apoptosis in OS cells. in vivo assays confirmed that knockdown of HOXD-AS1 significantly decreased tumor growth in xenograft mice, and decreased tumor size and weight. Importantly, we also showed that knockdown of HOXD-AS1 significantly reduced signal transducer and activator of transcription 3 and its target protein (CyclinD1, Bcl-2, and MMP-2) expression in vitro and in vivo. Moreover, overexpression of STAT3 could reverse the suppression of proliferation ability induced by sh-HOXD-AS1 in U2OS cells. Collectively, our data indicated that HOXD-AS1 might be an oncogenic long non-coding RNA (lncRNA) and might be a potential attractive therapeutic target for OS.

Long noncoding RNAs: emerging players in thyroid cancer pathogenesis

Thyroid cancer continues to be the most common malignancy of endocrine glands. The incidence of thyroid cancer has risen significantly over the past 4 decades and has emerged as a major health issue. In recent years, significant progress has been achieved in our understanding of the molecular mechanisms of thyroid carcinogenesis, resulting in significant diagnostic, prognostic and therapeutic implications; yet, it has not reached a satisfactory level. Identifying novel molecular therapeutic targets and molecules for diagnosis and prognosis is expected to advance the overall management of this common malignancy. Long noncoding RNAs (lncRNAs) are implicated in the regulation of various key cellular genes involved in cell differentiation, proliferation, cell cycle, apoptosis, migration and invasion mainly through modulation of gene expression. Recent studies have established that lncRNAs are deregulated in thyroid cancer. In this review, we discuss extensively the tumor-suppressive (for example, LINC00271, MEG3, NAMA, PTCSC1/2/3, etc.) and oncogenic (for example, ANRIL, FAL1, H19, PVT1, etc.) roles of various lncRNAs and their possible disease associations implicated in thyroid carcinogenesis. We briefly summarize the strategies and mechanisms of lncRNA-targeting agents. We also describe the potential role of lncRNAs as prospective novel therapeutic targets, and diagnostic and prognostic markers in thyroid cancer.

Molecular Network-Based Identification of Competing Endogenous RNAs in Thyroid Carcinoma

RNAs may act as competing endogenous RNAs (ceRNAs), a critical mechanism in determining gene expression regulations in many cancers. However, the roles of ceRNAs in thyroid carcinoma remains elusive. In this study, we have developed a novel pipeline called Molecular Network-based Identification of ceRNA (MNIceRNA) to identify ceRNAs in thyroid carcinoma. MNIceRNA first constructs micro RNA (miRNA)-messenger RNA (mRNA)long non-coding RNA (lncRNA) networks from miRcode database and weighted correlation network analysis (WGCNA), based on which to identify key drivers of differentially expressed RNAs between normal and tumor samples. It then infers ceRNAs of the identified key drivers using the long non-coding competing endogenous database (lnCeDB). We applied the pipeline into The Cancer Genome Atlas (TCGA) thyroid carcinoma data. As a result, 598 lncRNAs, 1025 mRNAs, and 90 microRNA (miRNAs) were inferred to be differentially expressed between normal and thyroid cancer samples. We then obtained eight key driver miRNAs, among which hsa-mir-221 and hsa-mir-222 were key driver RNAs identified by both miRNA-mRNA-lncRNA and WGCNA network. In addition, hsa-mir-375 was inferred to be significant for patients' survival with 34 associated ceRNAs, among which RUNX2, DUSP6 and SEMA3D are known oncogenes regulating cellular proliferation and differentiation in thyroid cancer. These ceRNAs are critical in revealing the secrets behind thyroid cancer progression and may serve as future therapeutic biomarkers.

Statistical analysis of non-coding RNA data

With rapid progress in high-throughput genome technology, the study of noncoding RNA has arisen as a highly popular topic in biomedical research. Noncoding RNA plays fundamental roles in cell proliferation, cell differentiation and epigenetic regulation, and the study of noncoding RNA will yield novel insights into gene regulation and provide new clues for disease treatment. However, due to the large volume and diverse functions of noncoding RNAs, the analysis of these RNAs has proved to be a challenging task. In this review, we review the commonly used computational tools for the identification of noncoding RNAs, and discuss popular statistical tools for their analysis. Due to the large body of noncoding RNA classes, we focus on the analysis of microRNA and long noncoding RNA, two of the most widely studied classes of noncoding RNAs. Specific examples are provided to show the context of the analysis. This review aims to provide up-to-date information on existing tools and methods for identifying and analyzing noncoding RNA.

Active Surveillance for Papillary Thyroid Microcarcinoma: Challenges and Prospects

Active surveillance (AS) can be considered as an alternative to immediate surgery in low-risk papillary thyroid microcarcinoma (PTMC) without clinically apparent lymph nodes, gross extrathyroidal extension (ETE), and/or distant metastasis according to American Thyroid Association. However, in the past AS has been controversial, as evidence supporting AS in the management of PTMC was scarce. The most prominent of these controversies included, the limited accuracy and utility of ultrasound (US) in the detection of ETE, malignant lymph node involvement or the advent of novel lymph node malignancy during AS, and disease progression. We summarized publications and indicated: (1) US, performer-dependent, could not accurately diagnose gross ETE or malignant lymph node involvement in PTMC. However, the combination of computed tomography and US provided more accurate diagnostic performance, especially in terms of selection sensitivity. (2) Compared to immediate surgery patients, low-risk PTMC patients had a slightly higher rate of lymph node metastases (LNM), although the overall rate for both groups remained low. (3) Recent advances in the sensitivity and specificity of imaging and incorporation of diagnostic biomarkers have significantly improved confidence in the ability to differentiate indolent vs. aggressive PTMCs. Our paper reviewed current imagings and biomarkers with initial promise to help select AS candidates more safely and effectively. These challenges and prospects are important areas for future research to promote AS in PTMC.