Dynamic TF-lncRNA Regulatory Networks Revealed Prognostic Signatures in the Development of Ovarian Cancer

"Crosstalk between non-coding RNAs and transcription factor LRF in non-small cell lung cancer"

Epigenetic approaches in direct correlation with assessment of critical genetic mutations in non-small cell lung cancer (NSCLC) are currently very intensive, as the epigenetic components underlying NSCLC development and progression have attained high recognition. In this level of research, established human NSCLC cell lines as well as experimental animals are widely used to detect novel biomarkers and pharmacological targets to treat NSCLC. The epigenetic background holds a great potential for the identification of epi-biomarkers for treatment response however, it is highly complex and requires precise definition as these phenomena are variable between NSCLC subtypes and systems origin. We engaged an in-depth characterization of non-coding (nc)RNAs prevalent in human KRAS-mutant NSCLC cell lines A549 and H460 and mouse KRAS-mutant NSCLC tissue by Next Generation Sequencing (NGS) and quantitative Real Time PCRs (qPCRs). Also, the transcription factor (TF) LRF, a known epigenetic silencer, was examined as a modulator of non-coding RNAs expression. Finally, interacting networks underlying epigenetic variations in NSCLC subtypes were created. Data derived from our study highlights the divergent epigenetic profiles of NSCLC of human and mouse origin, as well as the significant contribution of 12qf1: 109,709,060-109,747,960 mouse chromosomal region to micro-RNA upregulated species. Furthermore, the novel epigenetic miR-148b-3p/lncPVT1/ZBTB7A axis was identified, which differentiates human cell line of lung adenocarcinoma from large cell lung carcinoma, two characteristic NSCLC subtypes. The detailed recording of epigenetic events in NSCLC and combinational studies including networking between ncRNAs and TFs validate the identification of significant epigenetic features, prevailing in NSCLC subtypes and among experimental models. Our results enrich knowledge in the field and empower research on the epigenetic prognostic biomarkers of the disease progression, NSCLC subtypes discrimination and advancement to patient-tailored treatments.

Transcriptional repression of lncRNA and miRNA subsets mediated by LRF during erythropoiesis

Non-coding RNA (ncRNA) species, mainly long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been currently imputed for lesser or greater involvement in human erythropoiesis. These RNA subsets operate within a complex circuit with other epigenetic components and transcription factors (TF) affecting chromatin remodeling during cell differentiation. Lymphoma/leukemia-related (LRF) TF exerts higher occupancy on DNA CpG rich sites and is implicated in several differentiation cell pathways and erythropoiesis among them and also directs the epigenetic regulation of hemoglobin transversion from fetal (HbF) to adult (HbA) form by intervening in the γ-globin gene repression. We intended to investigate LRF activity in the evolving landscape of cells' commitment to the erythroid lineage and specifically during HbF to HbA transversion, to qualify this TF as potential repressor of lncRNAs and miRNAs. Transgenic human erythroleukemia cells, overexpressing LRF and further induced to erythropoiesis, were subjected to expression analysis in high LRF occupancy genetic loci-producing lncRNAs. LRF abundance in genetic loci transcribing for studied lncRNAs was determined by ChIP-Seq data analysis. qPCRs were performed to examine lncRNA expression status. Differentially expressed miRNA pre- and post-erythropoiesis induction were assessed by next-generation sequencing (NGS), and their promoter regions were charted. Expression levels of lncRNAs were correlated with DNA methylation status of flanked CpG islands, and contingent co-regulation of hosted miRNAs was considered. LRF-binding sites were overrepresented in LRF overexpressing cell clones during erythropoiesis induction and exerted a significant suppressive effect towards lncRNAs and miRNA collections. Based on present data interpretation, LRF's multiplied binding capacity across genome is suggested to be transient and associated with higher levels of DNA methylation. KEY MESSAGES: During erythropoiesis, LRF displays extensive occupancy across genetic loci. LRF significantly represses subsets of lncRNAs and miRNAs during erythropoiesis. Promoter region CpG islands' methylation levels affect lncRNA expression. MiRNAs embedded within lncRNA loci show differential regulation of expression.

Mutator-Derived lncRNA Landscape: A Novel Insight Into the Genomic Instability of Prostate Cancer

Background

Increasing evidence has emerged to reveal the correlation between genomic instability and long non-coding RNAs (lncRNAs). The genomic instability-derived lncRNA landscape of prostate cancer (PCa) and its critical clinical implications remain to be understood.

Methods

Patients diagnosed with PCa were recruited from The Cancer Genome Atlas (TCGA) program. Genomic instability-associated lncRNAs were identified by a mutator hypothesis-originated calculative approach. A signature (GILncSig) was derived from genomic instability-associated lncRNAs to classify PCa patients into high-risk and low-risk groups. The biochemical recurrence (BCR) model of a genomic instability-derived lncRNA signature (GILncSig) was established by Cox regression and stratified analysis in the train set. Then its prognostic value and association with clinical features were verified by Kaplan–Meier (K-M) analysis and receiver operating characteristic (ROC) curve in the test set and the total patient set. The regulatory network of transcription factors (TFs) and lncRNAs was established to evaluate TF–lncRNA interactions.

Results

A total of 95 genomic instability-associated lncRNAs of PCa were identified. We constructed the GILncSig based on 10 lncRNAs with independent prognostic value. GILncSig separated patients into the high-risk (n = 121) group and the low-risk (n = 121) group in the train set. Patients with high GILncSig score suffered from more frequent BCR than those with low GILncSig score. The results were further validated in the test set, the whole TCGA cohort, and different subgroups stratified by age and Gleason score (GS). A high GILncSig risk score was significantly associated with a high mutation burden and a low critical gene expression (PTEN and CDK12) in PCa. The predictive performance of our BCR model based on GILncSig outperformed other existing BCR models of PCa based on lncRNAs. The GILncSig also showed a remarkable ability to predict BCR in the subgroup of patients with TP53 mutation or wild type. Transcription factors, such as FOXA1, JUND, and SRF, were found to participate in the regulation of lncRNAs with prognostic value.

Conclusion

In summary, we developed a prognostic signature of BCR based on genomic instability-associated lncRNAs for PCa, which may provide new insights into the epigenetic mechanism of BCR.

Construction and validation of a transcription factors-based prognostic signature for ovarian cancer

BACKGROUND

Ovarian cancer (OC) is one of the most common and lethal malignant tumors worldwide and the prognosis of OC remains unsatisfactory. Transcription factors (TFs) are demonstrated to be associated with the clinical outcome of many types of cancers, yet their roles in the prognostic prediction and gene regulatory network in patients with OC need to be further investigated.

METHODS

TFs from GEO datasets were collected and analyzed. Differential expression analysis, WGCNA and Cox-LASSO regression model were used to identify the hub-TFs and a prognostic signature based on these TFs was constructed and validated. Moreover, tumor-infiltrating immune cells were analyzed, and a nomogram containing age, histology, FIGO_stage and TFs-based signature were established. Potential biological functions, pathways and the gene regulatory network of TFs in signature was also explored.

RESULTS

In this study, 6 TFs significantly associated with the prognosis of OC were identified. These TFs were used to build up a TFs-based signature for predicting the survival of patients with OC. Patients with OC in training and testing datasets were divided into high-risk and low-risk groups, according to the median value of risk scores determined by the signature. The two groups were further used to validate the performance of the signature, and the results showed the TFs-based signature had effective prediction ability. Immune infiltrating analysis was conducted and abundance of B cells naïve, T cells CD4 memory resting, Macrophages M2 and Mast cells activated were significantly higher in high-risk group. A nomogram based on the signature was established and illustrated good predictive efficiencies for 1, 2, and 3-year overall survival. Furthermore, the construction of the TFs-target gene regulatory network revealed the potential mechanisms of TFs in OC.

CONCLUSIONS

To our best knowledge, it is for the first time to develop a prognostic signature based on TFs in OC. The TFs-based signature is proven to be effective in predicting the survival of patients with OC. Our study may facilitate the clinical decision-making for patients with OC and help to elucidate the underlying mechanism of TFs in OC.

Identification and Characterization of TF-lncRNA Regulatory Networks Involved in the Tumorigenesis and Development of Adamantinomatous Craniopharyngioma

Craniopharyngiomas (CPs) are rare tumors arising from the sellar region. Although the best outcome for patients with one subtype, adamantinomatous craniopharyngioma (ACP), is obtained by gross total resection, little is known about the roles of long noncoding RNAs (lncRNAs) and transcription factors (TFs) in ACP tumorigenesis. In total, 12 human ACP and 5 control samples were subjected to transcriptome-level sequencing. We built an integrated algorithm for identifying lncRNAs and TFs regulating the CP-related pathway. Furthermore, ChIP-Seq datasets with binding domain information were used to further verify and identify TF-lncRNA correlations. RT–PCR and immunohistochemistry staining were performed to validate the potential targets. Five pathways associated with ACP were identified and defined by an extensive literature search. Based on the specific pathways and the whole gene expression profile, 266 ACP-related lncRNAs and 39 TFs were identified by our integrating algorithm. Comprehensive analysis of the ChIP-Seq datasets revealed that 29 TFs were targeted by 12000 lncRNAs in a wide range of tissues, including 161 ACP-related lncRNAs that were identified by the computational method. These 29 TFs and 161 lncRNAs, constituting 1004 TF-lncRNA pairs, were shown to potentially regulate different ACP-related pathways. A total of 232 TF-lncRNA networks were consequently established based on differential gene expression. Validation by RT–PCR and immunohistochemistry staining revealed positive expression of the ACP-related TFs E2F2 and KLF5 in ACP. Moreover, the expression of the lncRNA RP11-360P21.2 was shown to be upregulated in ACP tissues. In this study, we introduced an integrated algorithm for identifying lncRNAs and TFs regulating the ACP-related pathway. This is the first comprehensive study to systematically investigate the potential TF and lncRNA regulatory network in ACP. The resulting data serve as a valuable resource for understanding the mechanisms underlying ACP-related lncRNAs and TFs.

Characterizing the Copy Number Variation of Non-Coding RNAs Reveals Potential Therapeutic Targets and Prognostic Markers of LUSC

Lung squamous cell carcinoma (LUSC) has a poor clinical prognosis and a lack of available targeted therapies. Therefore, there is an urgent need to identify novel prognostic markers and therapeutic targets to assist in the diagnosis and treatment of LUSC. With the development of high-throughput sequencing technology, integrated analysis of multi-omics data will provide annotation of pathogenic non-coding variants and the role of non-coding sequence variants in cancers. Here, we integrated RNA-seq profiles and copy number variation (CNV) data to study the effects of non-coding variations on gene regulatory network. Furthermore, the 372 long non-coding RNAs (lncRNA) regulated by CNV were used as candidate genes, which could be used as biomarkers for clinical application. Nine lncRNAs including LINC00896, MCM8-AS1, LINC01251, LNX1-AS1, GPRC5D-AS1, CTD-2350J17.1, LINC01133, LINC01121, and AC073130.1 were recognized as prognostic markers for LUSC. By exploring the association of the prognosis-related lncRNAs (pr-lncRNAs) with immune cell infiltration, GPRC5D-AS1 and LINC01133 were highlighted as markers of the immunosuppressive microenvironment. Additionally, the cascade response of pr-lncRNA-CNV-mRNA-physiological functions was revealed. Taken together, the identification of prognostic markers and carcinogenic regulatory mechanisms will contribute to the individualized treatment for LUSC and promote the development of precision medicine.

ECM-Receptor Regulatory Network and Its Prognostic Role in Colorectal Cancer

Interactions of the extracellular matrix (ECM) and cellular receptors constitute one of the crucial pathways involved in colorectal cancer progression and metastasis. With the use of bioinformatics analysis, we comprehensively evaluated the prognostic information concentrated in the genes from this pathway. First, we constructed a ECM-receptor regulatory network by integrating the transcription factor (TF) and 5'-isomiR interaction databases with mRNA/miRNA-seq data from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD). Notably, one-third of interactions mediated by 5'-isomiRs was represented by noncanonical isomiRs (isomiRs, whose 5'-end sequence did not match with the canonical miRBase version). Then, exhaustive search-based feature selection was used to fit prognostic signatures composed of nodes from the network for overall survival prediction. Two reliable prognostic signatures were identified and validated on the independent The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) cohort. The first signature was made up by six genes, directly involved in ECM-receptor interaction: AGRN, DAG1, FN1, ITGA5, THBS3, and TNC (concordance index 0.61, logrank test p = 0.0164, 3-years ROC AUC = 0.68). The second hybrid signature was composed of three regulators: hsa-miR-32-5p, NR1H2, and SNAI1 (concordance index 0.64, logrank test p = 0.0229, 3-years ROC AUC = 0.71). While hsa-miR-32-5p exclusively regulated ECM-related genes (COL1A2 and ITGA5), NR1H2 and SNAI1 also targeted other pathways (adhesion, cell cycle, and cell division). Concordant distributions of the respective risk scores across four stages of colorectal cancer and adjacent normal mucosa additionally confirmed reliability of the models.

LncACTdb 3.0: an updated database of experimentally supported ceRNA interactions and personalized networks contributing to precision medicine

LncACTdb 3.0 is a comprehensive database of experimentally supported interactions among competing endogenous RNA (ceRNA) and the corresponding personalized networks contributing to precision medicine. LncACTdb 3.0 is freely available at http://bio-bigdata.hrbmu.edu.cn/LncACTdb or http://www.bio-bigdata.net/LncACTdb. We have updated the LncACTdb 3.0 database with several new features, including (i) 5669 experimentally validated ceRNA interactions across 25 species and 537 diseases/phenotypes through manual curation of published literature, (ii) personalized ceRNA interactions and networks for 16 228 patients from 62 datasets in TCGA and GEO, (iii) sub-cellular and extracellular vesicle locations of ceRNA manually curated from literature and data sources, (iv) more than 10 000 experimentally supported long noncoding RNA (lncRNA) biomarkers associated with tumor diagnosis and therapy, and (v) lncRNA/mRNA/miRNA expression profiles with clinical and pathological information of thousands of cancer patients. A panel of improved tools has been developed to explore the effects of ceRNA on individuals with specific pathological backgrounds. For example, a network tool provides a comprehensive view of lncRNA-related, patient-specific, and custom-designed ceRNA networks. LncACTdb 3.0 will provide novel insights for further studies of complex diseases at the individual level and will facilitate the development of precision medicine to treat such diseases.

Dissecting the Pathogenesis of Diabetic Retinopathy Based on the Biological ceRNA Network and Genome Variation Disturbance

Background

Diabetic retinopathy (DR) is the most important manifestation of diabetic microangiopathy. It is essential to explore the gene regulatory relationship and genomic variation disturbance of biological networks in DR progression.

Methods

In this study, we constructed a comprehensive lncRNA-mRNA ceRNA network of DR procession (CLMN) and explored its topological characteristics.

Results

Modular and functional analysis indicated that the organization of CLMN performed fundamental and specific functions in diabetes and DR pathology. The differential expression of hub ceRNA nodes and positive correlation reveals the highly connected ceRNA regulation and important roles in the regulating of DR pathology. A large proportion of SNPs in the TFBS, DHS, and enhancer regions of lncRNAs will affect lncRNA transcription and further cause expression variation. Some SNPs were found to disrupt the lncRNA functional elements such as miRNA target binding sites. These results indicate the complex nature of genotypic effects in the disturbing of CLMN and further contribute to gene expression variation and different disease phenotypes.

Conclusion

The identification of individual genomic variations and analysis of biological network disturbance by these genomic variations will help provide more personalized treatment plans and promote the development of precision medicine for DR.

Immune-related eight-lncRNA signature for improving prognosis prediction of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the leading cause of cancer‐related deaths worldwide. Therefore, the identification of a novel prediction signature for predicting the prognosis risk and survival outcomes is urgently demanded.

Methods

We integrated a machine‐learning frame by combing the Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression model to identify the LUAD‐related long non‐coding RNA (lncRNA) survival biomarkers. Subsequently, the Spearman correlation test was employed to interrogate the relationships between lncRNA signature and tumor immunity and constructed the competing endogenous RNA (ceRNA) network.

Results

Herein, we identified an eight‐lncRNA signature (PR‐lncRNA signature, NPSR1‐AS1, SATB2‐AS1, LINC01090, FGF12‐AS2, AC005256.1, MAFA‐AS1, BFSP2‐AS1, and CPC5‐AS1), which contributes to predicting LUAD patient's prognosis risk and survival outcomes. The PR‐lncRNA signature has also been confirmed as the robust signature in independent datasets. Further parsing of the LUAD tumor immune infiltration showed the PR‐lncRNAs were closely associated with the abundance of multiple immune cells infiltration and the expression of MHC molecules. Furthermore, by constructing the PR‐lncRNA–related ceRNA network, we interrogated more potential anti‐cancer therapy targets.

Conclusion

lncRNAs, as emerging cancer biomarkers, play an important role in a variety of cancer processes. Identification of PR‐lncRNA signatures allows us to better predict patient's survival outcomes and disease risk. Finally, the PR‐lncRNA signatures could help us to develop novel LUAD anti‐cancer therapeutic strategies.

Single-Cell Transcriptome Analysis in Melanoma Using Network Embedding

Single-cell sequencing technology provides insights into the pathology of complex diseases like cancer. Here, we proposed a novel computational framework to explore the molecular mechanisms of cancer called melanoma. We first constructed a disease-specific cell–cell interaction network after data preprocessing and dimensionality reduction. Second, the features of cells in the cell–cell interaction network were learned by node2vec which is a graph embedding technology proposed previously. Then, consensus clusters were identified by considering different clustering algorithms. Finally, cell markers and cancer-related genes were further analyzed by integrating gene regulation pairs. We exploited our model on two independent datasets, which showed interesting results that the differences between clusters obtained by consensus clustering based on network embedding (CCNE) were observed obviously through visualization. For the KEGG pathway analysis of clusters, we found that all clusters are extremely related to MicroRNAs in cancer and HTLV-I infection, and the hub genes in cluster specific regulatory networks, i.e., ETS1, TP53, E2F1, and GATA3 are highly associated with melanoma. Furthermore, our method can also be extended to other scRNA-seq data.

Identification of a Prognostic Signature for Ovarian Cancer Based on the Microenvironment Genes

Background: Ovarian cancer is highly malignant and has a poor prognosis in the advanced stage. Studies have shown that infiltration of tumor microenvironment cells, immune cells and stromal cells has an important impact on the prognosis of cancers. However, the relationship between tumor microenvironment genes and the prognosis of ovarian cancer has not been studied.

Methods: Gene expression profiles and SNP data of ovarian cancer were downloaded from the TCGA database. Cluster analysis, WGCNA analysis and univariate survival analysis were used to identify immune microenvironment genes as prognostic signatures for predicting the survival of ovarian cancer patients. External data were used to evaluate the signature. Moreover, the top five significantly correlated genes were evaluated by immunohistochemical staining of ovarian cancer tissues.

Results: We systematically analyzed the relationship between ovarian cancer and immune metagenes. Immune metagenes expression were associated with prognosis. In total, we identified 10 genes related to both immunity and prognosis in ovarian cancer according to the expression of immune metagenes. These data reveal that high expression of ETV7 (OS, HR = 1.540, 95% CI 1.023–2.390, p = 0.041), GBP4 (OS, HR = 1.834, 95% CI 1.242–3.055, p = 0.004), CXCL9 (OS, HR = 1.613, 95% CI 1.080 –2.471, p = 0.021), CD3E (OS, HR = 1.590, 95% CI 1.049 –2.459, p = 0.031), and TAP1 (OS, HR = 1.766, 95% CI 1.163 –2.723, p = 0.009) are associated with better prognosis in patients with ovarian cancer.

Conclusion: Our study identified 10 immune microenvironment genes related to the prognosis of ovarian cancer. The list of tumor microenvironment-related genes provides new insights into the underlying biological mechanisms driving the tumorigenesis of ovarian cancer.

A Panel of Five-lncRNA Signature as a Potential Biomarker for Predicting Survival in Gastric and Thoracic Cancers

Dysfunctional long non-coding RNAs (lncRNAs) have been found to have carcinogenic and/or tumor inhibitory effects in the development and progression of cancer, suggesting their potential as new independent biomarkers for cancer diagnosis and prognosis. The exploration of the relationship between lncRNAs and the overall survival (OS) of different cancers opens up new prospects for tumor diagnosis and treatment. In this study, we established a five-lncRNA signature and explored its prognostic efficiency in gastric cancer (GC) and several thoracic malignancies, including breast invasive carcinoma (BRCA), esophageal carcinoma, lung adenocarcinoma, lung squamous cell carcinoma (LUSC), and thymoma (THYM). Cox regression analysis and lasso regression were used to evaluate the relationship between lncRNA expression and survival in different cancer datasets from GEO and TCGA. Kaplan-Meier survival curves indicated that risk scores characterized by a five-lncRNA signature were significantly associated with the OS of GC, BRCA, LUSC, and THYM patients. Functional enrichment analysis showed that these five lncRNAs are involved in known biological pathways related to cancer pathology. In conclusion, the five-lncRNA signature can be used as a prognostic marker to promote the diagnosis and treatment of GC and thymic malignancies.

Identification of LncRNA Prognostic Markers for Ovarian Cancer by Integration of Co-expression and CeRNA Network

Background

Ovarian cancer (OC), one of the most prevalent gynecological malignancies, is characterized by late detection and dismal prognosis. Recent studies show that long non-coding RNAs (lncRNAs) in competitive endogenous RNA (ceRNA) networks influence immune infiltration and cancer prognosis. However, the function of lncRNA in OC immune infiltration and prognosis remains unclear.

Methods

Transcriptomes of 378 OC samples and clinical data were retrieved from the TCGA repository. Modules related to immune cells were identified using weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis and survival analysis were then performed for the identification of immune-related lncRNAs in the brown module using Cox regression model. Finally, a ceRNA network was constructed by using the lncRNAs and mRNAs from the brown module.

Results

We found lncRNAs and mRNAs in the brown module to be significantly associated with immune cells in OC and identified 4 lncRNAs as potential OC prognostic markers. We further established that lncRNAs in the ceRNA network influence OC immune infiltration and prognosis by regulating miRNA, ultimately modulating mRNA levels.

Conclusion

We have identified 4 lncRNAs as independent immune prognostic factors for OC. Furthermore, our findings offer novel insight into lncRNAs as OC immune and prognostic biomarkers.

LnCeCell: a comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution

Within the tumour microenvironment, cells exhibit different behaviours driven by fine-tuning of gene regulation. Identification of cellular-specific gene regulatory networks will deepen the understanding of disease pathology at single-cell resolution and contribute to the development of precision medicine. Here, we describe a database, LnCeCell (http://www.bio-bigdata.net/LnCeCell/ or http://bio-bigdata.hrbmu.edu.cn/LnCeCell/), which aims to document cellular-specific long non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) networks for personalised characterisation of diseases based on the ‘One Cell, One World’ theory. LnCeCell is curated with cellular-specific ceRNA regulations from >94 000 cells across 25 types of cancers and provides >9000 experimentally supported lncRNA biomarkers, associated with tumour metastasis, recurrence, prognosis, circulation, drug resistance, etc. For each cell, LnCeCell illustrates a global map of ceRNA sub-cellular locations, which have been manually curated from the literature and related data sources, and portrays a functional state atlas for a single cancer cell. LnCeCell also provides several flexible tools to infer ceRNA functions based on a specific cellular background. LnCeCell serves as an important resource for investigating the gene regulatory networks within a single cell and can help researchers understand the regulatory mechanisms underlying complex microbial ecosystems and individual phenotypes.