Integrated analysis identifying new lncRNA markers revealed in ceRNA network for tumor recurrence in papillary thyroid carcinoma and build of nomogram

LncRNA FAM225B Regulates PDIA4-Mediated Ovarian Cancer Cell Invasion and Migration via Modulating Transcription Factor DDX17

Objective

This study aimed to explore the roles and mechanisms of lncRNA FAM225B and PDIA4 in ovarian cancer.

Methods

RT-qPCR and Western blot assays were performed to detect the expression levels of the lncRNAs FAM225B, DDX17, and PDIA4 in the serum of patients with ovarian cancer and cell lines. Cells were transfected with lncRNA FAM225B- and PDIA4-related vectors to determine the malignant phenotypes using functional experiments. The mutual binding of lncRNA FAM225B and DDX17 was verified using RNA pull-down and RIP assays.

Results

The expression of lncRNAs FAM225B and PDIA4 was decreased in the serum of patients with ovarian cancer and cell lines. Restoration of lncRNA FAM225B or PDIA4 reduced cell proliferation, migration, and invasion abilities and elevated the apoptosis rate, whereas suppression of lncRNA FAM225B or PDIA4 exhibited an inverse trend. RNA pull-down and RIP assays revealed a direct interaction between lncRNA FAM225B and DDX17. ChIP assay revealed a relationship between DDX17 and the PDIA4 promoter. LncRNA FAM225B and DDX17 positively regulate PDIA4 expression. Downregulation of PDIA4 expression counteracts the suppressive effect of lncRNA FAM225B overexpression in ovarian cancer cells.

Conclusion

This research study supports the fact that lncRNA FAM225B in ovarian cancer can upregulate PDIA4 by directly binding to DDX17, inhibiting the activities of ovarian cancer cells.

Identification of potential LncRNAs as papillary thyroid carcinoma biomarkers based on integrated bioinformatics analysis using TCGA and RNA sequencing data

The roles and mechanisms of long non-coding RNAs (lncRNAs) in papillary thyroid cancer (PTC) remain elusive. We obtained RNA sequencing (RNA-seq) data of surgical PTC specimens from patients with thyroid cancer (THCA; n = 20) and identified differentially expressed genes (DEGs) between cancer and cancer-adjacent tissue samples. We identified 2309 DEGs (1372 significantly upregulated and 937 significantly downregulated). We performed Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment, and protein-protein interaction network analyses and screened for hub lncRNAs. Using the same methods, we analyzed the RNA-seq data from THCA dataset in The Cancer Genome Atlas (TCGA) database to identify differentially expressed lncRNAs. We identified 15 key differentially expressed lncRNAs and pathways that were closely related to PTC. Subsequently, by intersecting the differentially expressed lncRNAs with hub lncRNAs, we identified LINC02407 as the key lncRNA. Assessment of the associated clinical characteristics and prognostic correlations revealed a close correlation between LINC02407 expression and N stage of patients. Furthermore, receiver operating characteristic curve analysis showed that LINC02407 could better distinguish between cancerous and cancer-adjacent tissues in THCA patients. In conclusion, our findings suggest that LINC02407 is a potential biomarker for PTC diagnosis and the prediction of lymph node metastasis.

Thymic epithelial tumors: examining the GTF2I mutation and developing a novel prognostic signature with LncRNA pairs to predict tumor recurrence

Background

General transcription factor IIi (GTF2I) mutations are very common in thymic epithelial tumors (TETs) and are related to a more favorable prognosis in TET patients. However, limited research has been conducted on the role of GTF2I in the tumor immune microenvironment (TIME). Further, long non-coding RNAs (lncRNAs) have been associated with the survival of patients with TETs. Therefore, this study aimed to explore the relationship between GTF2I mutations and TIME and build a new potential signature for predicting tumor recurrence in the TETs. Research data was downloaded from The Cancer Genome Atlas database and the CIBERSORT algorithm was used to evaluate TIME differences between GTF2I mutant and wild-type TETs. Relevant differentially expressed lncRNAs based on differentially expressed immune-related genes were identified to establish lncRNA pairs. We constructed a signature using univariate and multivariate Cox regression analyses.

Results

GTF2I is the most commonly mutated gene in TETs, and is associated with an increased number of early-stage pathological types, as well as no history of myasthenia gravis or radiotherapy treatment. In the GTF2I wild-type group, immune score and immune cell infiltrations with M2 macrophages, activated mast cells, neutrophils, plasma, T helper follicular cells, and activated memory CD4 T cells were higher than the GTF2I mutant group. A risk model was built using five lncRNA pairs, and the 1-, 3-, and 5-year area under the curves were 0.782, 0.873, and 0.895, respectively. A higher risk score was related to more advanced histologic type.

Conclusion

We can define the GTF2I mutant-type TET as an immune stable type and the GTF2I wild-type as an immune stressed type. A signature based on lncRNA pairs was also constructed to effectively predict tumor recurrence.

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.

Identification of Hub lncRNAs Along With lncRNA-miRNA-mRNA Network for Effective Diagnosis and Prognosis of Papillary Thyroid Cancer

Long noncoding RNAs (lncRNAs) play important roles in tumorigenesis and progression of different cancers and they have been potential biomarkers for cancer diagnosis and prognosis. As the most common endocrine malignancy, precise diagnosis and prognosis of papillary thyroid cancer (PTC) is of great clinical significance. Here, we aim to identify new hub lncRNAs for marking PTC and constructed prognostics signatures based on lncRNA- miRNA-mRNA competing endogenous RNAs (ceRNA) network to predict overall survival (OS) and disease-free survival (DFS) respectively. Five reliable hub lncRNAs were identified by integrating differential genes of four Gene Expression Omnibus (GEO) gene chips using the RobustRankAggreg (RRA) method. Based on differential analyses and interaction prediction, a lncRNA-mRNA co-expression network and a lncRNA-miRNA-mRNA ceRNA network were established. Then a comprehensive function characterization of the five hub lncRNAs was performed, including validation dataset testing, receiver operating characteristic (ROC) curve analysis, and functional analysis on two networks. All results suggest that these five hub lncRNAs could be potential biomarkers for marking PTC. The ceRNA network was used to identify RNAs which were associated with PTC prognosis. Two prognostic signatures were developed using univariate and step-wise multivariate Cox regression analyses and both of them were independent prognostic indicators for PTC OS and DFS. Tumor microenvironment difference analysis between high and low-risk patients showed that dendritic cells activated and macrophages M0 may be a possible target for immunotherapy of PTC. In addition, disclosing the potential drugs that may reverse the expression of hub genes may improve the prognosis of patients with PTC. Here, connectivity map (CMap) analysis indicates that three bioactive chemicals (pioglitazone, benserazide, and SB-203580) are promising therapeutic agents for PTC. So, the paper presents a comprehensive study on diagnosis, prognosis, and potential drug screening for PTC based on the five hub lncRNAs identified by us.

The Emerging Landscapes of Long Noncoding RNA in Thyroid Carcinoma: Biological Functions and Clinical Significance

Thyroid carcinoma (TC) is one of the most prevalent primary endocrine tumors, and its incidence is steadily and gradually increasing worldwide. Accumulating evidence has revealed the critical functions of long noncoding RNAs (lncRNAs) in the tumorigenesis and development of TC. Many TC-associated lncRNAs have been documented to be implicated in TC malignant behaviors, including abnormal cell proliferation, enhanced stem cell properties and aggressiveness, and resistance to therapeutics, through interaction with proteins, DNA, or RNA or encoding small peptides. Therefore, further elucidating the lncRNA dysregulation sheds additional insights into TC tumorigenesis and progression and opens new avenues for the early diagnosis and clinical therapy of TC. In this review, we summarize the abnormal expression of lncRNA in TC and the fundamental characteristics in TC tumorigenesis and development. Additionally, we introduce the potential prognostic and therapeutic significance of lncRNAs in TC.

Individualized Prediction of Survival by a 10-Long Non-coding RNA-Based Prognostic Model for Patients With Breast Cancer

Deregulations of long non-coding RNAs (lncRNAs) have been implicated in the progression of breast cancer (BC). However, the prognostic values of those lncRNAs in BC remain elusive. This study aimed at constructing a lncRNA-based prognostic model to improve the clinical management of BC. Systematic investigation of lncRNA expression profiles and clinical data from The Cancer Genome Atlas (TCGA) database were utilized to establish a 10-lncRNA signature. The prognostic signature efficiently discriminated patients with significantly different prognosis regardless of intrinsic molecular subtypes and tumor-node-metastasis (TNM) stage. A combined model was constructed by multivariate Cox proportional hazards regression (CPHR) analysis, which combined the lncRNA-based signature with certain clinical risk factors (TNM stage, age, and human epidermal growth factor receptor 2 status). This model predicted a survival probability that closely corresponds to the actual survival probability. With respect to the entire set, the time-dependent receiver-operating characteristic curves revealed that the area under the curve of this model was the highest than any of the clinical risk factors. Moreover, functional enrichment analysis indicated that the molecular signature was mainly involved in DNA replication, which was firmly related to BC tumorigenesis. Consistent with the discovery, the knockdown of LHX1-DT, one of the 10 prognostic lncRNAs, attenuated the proliferation of BC cells in vitro and in vivo. Taken together, our study constructed a novel 10-lncRNA signature for prediction prognosis, and the signature-based model could provide new insight into accurate management of BC patients.

Prognostic lncRNA, miRNA, and mRNA Signatures in Papillary Thyroid Carcinoma

The current focus in the treatment of papillary thyroid carcinoma (PTC) is tumor progression. The aim of this study was to build RNA-based classifiers and develop a comprehensive model to provide progression-free interval (PFI) risk prediction for PTC. The RNAseq data, miRNAseq data, and clinical information of PTC were downloaded from The Cancer Genome Atlas database. Based on the differently expressed RNAs, the least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to build the RNA-based classifiers for PFI of the patients with PTC. A 6-messenger RNA (mRNA)-based classifier, a 5-long non-coding RNA (lncRNA)-based classifier, and a 4-microRNA (miRNA)-based classifier were constructed to predict the PFI. Patients with high risk based on the constructed RNA-based classifiers had worse prognosis in Kaplan–Meier curve analysis with log-rank test. The areas under the curves of the first, third, and fifth years in the training and testing set were 0.83, 0.82, and 0.82 and 0.67, 0.72, and 0.73 for the 6-mRNA-based classifier, respectively; 0.75, 0.84, and 0.85 and 0.71, 0.67, and 0.71 for the 5-lncRNA-based classifier, respectively; and 0.70, 0.77, and 0.79 and 0.74, 0.67, and 0.66 for the 4-miRNA-based classifier, respectively. The prediction capability of the three RNA-based classifiers was superior to the TNM stage system. Furthermore, a nomogram based on the verified independent prognostic factors was established for the prognostic prediction. The C-index and calibration plots indicated good predictive accuracy of the nomogram. In summary, the 6-mRNA-based classifier and 5-lncRNA-based classifier constructed in this study were independent prognostic factors for PTC.

Microarray expression profiles and bioinformatics analysis of mRNAs, lncRNAs, and circRNAs in the secondary temozolomide-resistant glioblastoma

Temozolomide is a first line anti-tumor drug used for the treatment of patients with Glioblastoma multiforme (GBM). However, the drug resistance to temozolomide limits its clinical application. Therefore, novel strategies to overcome chemoresistance are desperately needed for improved treatment of human GBM. Here, we simultaneously detected, for the first time, the expression profiles of mRNAs, lncRNAs, and circRNAs in three pairs of secondary temozolomide-resistant glioblastoma (STRG) and matched primary glioblastoma tissues by microarrays. Using these data, we discovered a total of 92 mRNA, 299 lncRNAs and 53 circRNAs were altered in human glioma tissue after chemotherapy with temozolomide. The functions of differentially expressed lncRNAs, circRNAs were annotated by analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The results showed that the highest enriched GO terms of the upregulated lncRNAs were embryonic forelimb morphogenesis (BP), extracellular space (CC), and serine-type endopeptidase activity (MF). Meanwhile, GO:0035360(BP), PRC1 complex (CC), and ubiquitin-protein transferase activity (MF) were the highest enriched GO terms targeted by downregulated lncRNAs. The NF-kappa B signaling pathway were significantly enriched in the STRG. However, circRNAs highest enriched GO term was viral process, chromosome, and protein transporter activity, respectively. KEGG pathway analysis showed that circRNAs in the network were enriched in ErbB signaling pathway. Furthermore, we also predicted the potential role of these differentially expressed ncRNAs and constructed a network of lncRNAs-mRNAs and circRNAs-miRNAs to show their interactions. After a series of bioinformatics analyses, we found that low expression of NONHSAT163779 and high expression of circ_0043949 are closely related to the chemoresistance of STRG. Our findings revealed the alteration of expression patterns of mRNAs, lncRNAs, and circRNAs in the secondary temozolomide-resistant glioblastoma for the first time. NONHSAT163779 and hsa_circ_0043949 might be potential therapeutic targets and prognostic biomarkers for the treatment of glioblastoma.