LncSEA: a platform for long non-coding RNA related sets and enrichment analysis

Characterization and integrated analysis of extrachromosomal DNA amplification in hematological malignancies

The study of extrachromosomal DNA (ecDNA), an element existing beyond classical chromosomes, contributes to creating a more comprehensive map of the cancer genome. In hematological malignancies, research on ecDNA has lacked comprehensive investigation into its frequency, structure, function, and mechanisms of formation. We re-analyzed WGS data from 208 hematological cancer samples across 11 types, focusing on ecDNA characteristics. Amplification of ecDNA was observed in 7 of these cancer types, with no instances found in normal blood cells. Patients with leukemia carrying ecDNA showed a low induction therapy remission rate (<30 %), a high relapse rate (75 %) among those who achieved complete remission, and a significantly lower survival rate compared to the general leukemia population, even those with complex chromosomal karyotypes. Among the 55 identified ecDNA amplicons, 268 genes were detected, of which 38 are known cancer-related genes exhibiting significantly increased copy numbers. By integrating RNA-Seq data, we discovered that the increased copy number, resulting in a higher amount of available DNA templates, indeed leads to the elevated expression of genes encoded on ecDNA. Additionally, through the integration of H3K4me3/H3K27ac chromatin immunoprecipitation sequencing, assay for transposase-accessible chromatin with sequencing, and high-throughput chromosome conformation capture data, we identified that ecDNA amplifications can also facilitate efficient, copy number-independent amplification of oncogenes. This process is linked to active histone modifications, improved chromatin accessibility, and enhancer hijacking, all of which are effects of ecDNA amplification. Mechanistically, chromothripsis and dysfunction of the DNA repair pathway can, to some extent, explain the origin of ecDNA.

Discovery of high-expressing lncRNA-derived sORFs as potential tumor-associated antigens in hepatocellular carcinoma

BACKGROUND

This study is based on deep mining of Ribo-seq data for the identification of lncRNAs that have highly expressed sORFs in HCC. In this paper, dynamic prospects associated with sORFs acting as newly defined tumor-specific epitopes are discussed with possible improvement in strategies for tumor immunotherapy.

OBJECTIVE

Using ribosome profiling to identify and characterize sORFs within lncRNAs in HCC, identify potential therapeutic targets and tumor-specific epitopes applicable for immunotherapy.

METHODS

MetamORF performed the identification of sORFs with deep analysis of the data of ribosome profiling in lncRNAs associated with HCC. The translation efficiency in these molecules was estimated, and epitope prediction was done by pVACbind. Peptide search was done to check the presence of micropeptides translated from these identified sORFs. validated translational activity and identified potential epitopes.

RESULTS

Higher translation efficiency was noted in the case of lncRNAs associated with HCC compared to normal tissues. Of particular note is ORF3418981, which results in the highest expression and has supporting experimental evidence at the protein level. Epitope prediction identified a putative epitope at the C-terminus of ORF3418981.

CONCLUSIONS

This study uncovers the as-yet-unknown potential of lncRNA-derived sORFs as sources of tumor antigens, shifting the research focus from protein-coding genes to non-coding RNAs also in the HCC context. Moreover, this study highlights the contribution of a subset of lncRNAs, especially LINC00152, to the development of tumors and modulation of the immune response by its sORFs.

A methylation-related lncRNA-based prediction model in lung adenocarcinomas

BACKGROUND

The collaboration between methylation and the lung adenocarcinoma (LUAD) occurrence and development is closes. Long noncoding RNA (lncRNA), as a regulatory factor of various biological functions, can be used for cancer diagnosis. Our study aimed to construct a robust methylation-related lncRNA signature of LUAD.

METHODS

In the Cancer Genome Atlas (TCGA) dataset, we download the RNA expression data and clinical information of LUAD cases. To develop the best prognostic signature based on methylation-related lncRNAs, Cox regression analyses were utilized. Using Kaplan-Meier analysis, overall survival rates were compared between risk category included both low- and high-risk patients. To categorize genes according to their functional significance, GSEA (Subramanian et al, 2005) was used. Single-sample gene set enrichment analysis (ssGSEA) was used to further reveal the potential molecular mechanism of the methylation-related lncRNA prognostic model in immune infiltration. Using TRLnc (http://www.licpathway.net/TRlnc) and lncRNASNP to analyse the SNP sites and TRLnc of these 18 lncRNAs. LncSEA website was used to analyse 18 lncRNA in the process of tumour development and development. Go was used to analyse the enriched pathways enriched by TFs (transcription factors), Cerna networks, and proteins bound to each other of these 18 lncRNAs. The 'prophetic' package was used to analyse the value of this prognostic model in guiding personalized immunotherapy.

RESULTS

In this study, we identified 18 methylation-related lncRNAs (AP002761.1, AL118558.3, CH17-340M24.3, AL353150.1, AC004687.1, LINC00996, AF186192.1, HSPC324, AC087752.3, FAM30A, AC106047.1, AC026355.1, ABALON, LINC01843, AL606489.1, NKILA, AP001453.2, GSEC) to establish a methylation-related lncRNA signature that can detect patients prognosis in LUAD. The enriched pathways enriched by proteins interacting with 18 lncRNAs are mainly EMT, hypoxia, stemness and proliferation, among which LINC00996 and AF186192.1 are regulated by multiple tumour associated transcription factors, such as TP53 and TP63, and fam30a and mRNA form a Cerna network. There are 2319 SNP loci in LINC00996, 36 of which are risk SNP loci and 205 SNP loci in af186192.1; AF186192.1 affects 95 conserved miRNAs and 123 non-conserved miRNAs, promotes the binding of 149 pairs of miRNAs: lncRNAs and inhibits the binding of 95 pairs of miRNAs: lncRNAs. The ROC curve demonstrated that the established methylation-related lncRNA signature was more effective in predicting the prognosis of patients in LUAD than the clinicopathological parameters. Our research has confirmed that patients in the high-risk group which was separated by the risk score model based on methylation-related lncRNA had shorter OS. According to GSEA, the high-risk group had a predominantly tumour- and immune-related pathway enrichment. A significant association was shown by ssGSEA between predictive signature and immune status in LUAD patients. In addition, principal component analysis (PCA) demonstrated the prognostic and predictive value of our signature. The correlation between the predictive signature of methylation-related lncRNA and IC50 of conventional chemotherapy drugs can provide personalized chemotherapy regimens for LUAD patients. Methylation-related lncRNA signature can effectively predict DFS of patients in LUAD.

Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration

Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.

Upregulation of the long noncoding RNA GJA9-MYCBP and PVT1 is a potential diagnostic biomarker for acute lymphoblastic leukemia

BACKGROUND

Acute lymphoblastic leukemia (ALL) is the most common type of blood cancer in children. Aberrant expression of long noncoding RNAs (lncRNAs) may set stages for ALL development. LncRNAs are emerging as a novel diagnostic and prognostic biomarker for ALL. Herein, we aimed to evaluate the expression of lncRNA GJA9-MYCBP and PVT1 in blood samples of ALL and healthy individuals.

METHODS

As a case-control study, 40 pairs of ALL and healthy individual samples were used. The expression of MYC and each candidate lncRNA was measured using quantitative real-time PCR. Any possible association between the expression of putative noncoding RNAs and clinicopathological characteristics was also evaluated.

RESULTS

LncRNA GJA9-MYCBP and PVT1 were significantly upregulated in ALL samples compared with healthy ones. Similarly, mRNA levels of MYC were increased in ALL samples than control ones. Receiver operating characteristic curve analysis indicated a satisfactory diagnostic efficacy (p-value <.0001), suggesting that lncRNA GJA9-MYCBP and PVT1 may serve as a diagnostic biomarker for ALL. Linear regression analysis unveiled positive correlations between the expression level of MYC and lncRNA GJA9-MYCBP and PVT1 in ALL patients (p-values <.01).

CONCLUSIONS

In this study, we provided approval for the clinical diagnostic significance of lncRNA GJA9-MYCBP and PVT1that their upregulations may be a diagnostic biomarker for ALL.

Genome-wide investigation of lncRNAs revealed their tight association with gastric cancer

BACKGROUND

Gastric cancer (GC) is a significant health issue globally, ranking as the fifth most common cancer with over 10,000 new cases reported annually. Long non-coding RNA (lncRNA) has emerged as a critical player in cellular functions, influencing GC's development, growth, metastasis, and prognosis. However, our understanding of lncRNA's role in the pathogenesis of GC remains limited. Therefore, it is particularly important to explore the relationship between lncRNA and gastric cancer.

METHODS

we conducted a comprehensive analysis of RNA sequencing data from the GEO database and stomach adenocarcinoma (STAD) data from the TCGA database to identify lncRNAs that exhibit altered expression levels in GC and the mechanisms underlying lncRNA-mediated transcription and post-transcriptional regulation were explored.

RESULTS

This study uncovered 94 lncRNAs with differential expression and, through co-expression analysis, linked these to 1508 differentially expressed genes (DEGs). GO functional enrichment analysis highlighted that these DEGs are involved in critical pathways, such as cell adhesion and the positive regulation of cell migration. By establishing a lncRNA-miRNA-mRNA regulatory network, we found that the ceRNA mechanism, particularly involving RP11-357H14.17 and CTD-2377D24.4, could play a role in GC progression. Experimental validation of selected differentially expressed lncRNAs and mRNAs (including RP11-357H14.17-CLDN1, BBOX1, TRPM2-AS, CLDN1, PLAU, HOXB7) confirmed the RNA-seq results.

CONCLUSIONS

Overall, our findings highlight the critical role of the lncRNA-mRNA regulatory network in the development and progression of GC, offering potential biomarkers for diagnosis and targets for innovative treatment strategies.

Homeobox regulator Wilms Tumour 1 is displaced by androgen receptor at cis-regulatory elements in the endometrium of PCOS patients

Decidualisation, the process whereby endometrial stromal cells undergo morphological and functional transformation in preparation for trophoblast invasion, is often disrupted in women with polycystic ovary syndrome (PCOS) resulting in complications with pregnancy and/or infertility. The transcription factor Wilms tumour suppressor 1 (WT1) is a key regulator of the decidualization process, which is reduced in patients with PCOS, a complex condition characterized by increased expression of androgen receptor in endometrial cells and high presence of circulating androgens. Using genome-wide chromatin immunoprecipitation approaches on primary human endometrial stromal cells, we identify key genes regulated by WT1 during decidualization, including homeobox transcription factors which are important for regulating cell differentiation. Furthermore, we found that AR in PCOS patients binds to the same DNA regions as WT1 in samples from healthy endometrium, suggesting dysregulation of genes important to decidualisation pathways in PCOS endometrium due to competitive binding between WT1 and AR. Integrating RNA-seq and H3K4me3 and H3K27ac ChIP-seq metadata with our WT1/AR data, we identified a number of key genes involved in immune response and angiogenesis pathways that are dysregulated in PCOS patients. This is likely due to epigenetic alterations at distal enhancer regions allowing AR to recruit cofactors such as MAGEA11, and demonstrates the consequences of AR disruption of WT1 in PCOS endometrium.

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

BACKGROUND

Prompt identification and assessment of the disease are essential for reducing the death rate associated with colorectal cancer (COL). Identifying specific causal or sensitive components, such as coding RNA (cRNA) and non-coding RNAs (ncRNAs), may greatly aid in the early detection of colorectal cancer.

METHODS

For this purpose, we gave natural chemicals obtained from Sparassis latifolia (SLPs) either alone or in conjunction with chemotherapy (5-Fluorouracil to a mouse colorectal tumor model induced by AOM-DSS. The transcription profile of non-coding RNAs (ncRNAs) and their target hub genes was evaluated using qPCR Real-Time, and ELISA techniques.

RESULTS

MSX2, MMP7, ITIH4, and COL1A2 were identified as factors in inflammation and oxidative stress, leading to the development of COL. The hub genes listed, upstream regulatory factors such as lncRNA PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p have been discovered as biomarkers for prognosis and diagnosis of COL. The SLPs and exercise, effectively decreased the size and quantity of tumors.

CONCLUSIONS

This effect may be attributed to the modulation of gene expression levels, including MSX2, MMP7, ITIH4, COL1A2, PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p. Ultimately, SLPs and exercise have the capacity to be regarded as complementing and enhancing chemotherapy treatments, owing to their efficacious components.

Emerging roles and mechanisms of lncRNAs in fruit and vegetables

With the development of genome sequencing technologies, many long non-coding RNAs (lncRNAs) have been identified in fruit and vegetables. lncRNAs are primarily transcribed and spliced by RNA polymerase II (Pol II) or plant-specific Pol IV/V, and exhibit limited evolutionary conservation. lncRNAs intricately regulate various aspects of fruit and vegetables, including pigment accumulation, reproductive tissue development, fruit ripening, and responses to biotic and abiotic stresses, through diverse mechanisms such as gene expression modulation, interaction with hormones and transcription factors, microRNA regulation, and involvement in alternative splicing. This review presents a comprehensive overview of lncRNA classification, basic characteristics, and, most importantly, recent advances in understanding their functions and regulatory mechanisms.

Bioinformatics analysis of a disease-specific lncRNA-miRNA-mRNA regulatory network in recurrent spontaneous abortion (RSA)

BACKGROUND

This study investigated the molecular mechanisms of long non-coding RNAs (lncRNAs) in RSA using the lncRNA-miRNA-mRNA regulatory network.

METHODS

The present study obtained expression datasets of long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), and microRNAs (miRNAs) from blood samples of individuals with unexplained recurrent spontaneous abortion (RSA) and healthy controls. Differentially expressed lncRNAs (DELs), mRNAs (DEMs), and miRNAs (DEmiRs) were identified. A regulatory network comprising lncRNA, miRNA, and mRNA was constructed, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEM. Also, a protein-protein interaction (PPI) network was made and key genes were identified.

RESULTS

A total of 57 DELs, 212 DEmiRs, and 301 DEMs regarding RSA were identified. Later analysis revealed a lncRNA-miRNA-mRNA network comprising nine lncRNAs, 14 miRNAs, and 65 mRNAs. Then, the ceRNA network genes were subjected to functional enrichment and pathway analysis, which showed their association with various processes, such as cortisol and thyroid hormone synthesis and secretion, human cytomegalovirus infection, and parathyroid hormone synthesis. In addition, ten hub genes (ITGB3, GNAI2, GNAS, SRC, PLEC, CDC42, RHOA, RAC1, CTNND1, and FN1) were identified based on the PPI network results.

CONCLUSION

In summary, the outcomes of our study provided some data regarding the alteration genes involved in RSA pathogenic mechanism via the lncRNA-miRNA-mRNA network and reveal the possibility of identifying new lncRNAs and miRNAs as promising molecular biomarkers.

Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach

Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting cancer subtypes with high accuracy and identifying genes, enhancers, and stable cell types simultaneously from sparse single-cell epigenomics data. The advantage of using a topic model is that it not only serves as a clustering algorithm, but it can also explain clustering results by providing word probability distributions over topics. Our study proposes a novel topic modeling approach for clustering single cells and detecting topics (gene signatures) in single-cell datasets that measure multiple omics simultaneously. We applied this approach to examine the transcriptional heterogeneity of luminal and triple-negative breast cancer cells using patient-derived xenograft models with acquired resistance to chemotherapy and targeted therapy. Through this approach, we identified protein-coding genes and long non-coding RNAs (lncRNAs) that group thousands of cells into biologically similar clusters, accurately distinguishing drug-sensitive and -resistant breast cancer types. In comparison to standard state-of-the-art clustering analyses, our approach offers an optimal partitioning of genes into topics and cells into clusters simultaneously, producing easily interpretable clustering outcomes. Additionally, we demonstrate that an integrative clustering approach, which combines the information from mRNAs and lncRNAs treated as disjoint omics layers, enhances the accuracy of cell classification.

Identification of key immune cells infiltrated in lung adenocarcinoma microenvironment and their related long noncoding RNA

LncRNA associated with immune cell infiltration in tumor microenvironment (TME) may be a potential therapeutic target for lung adenocarcinoma. We established a machine learning (ML) model based on 3896 samples characterized by the degree of immune cell infiltration, and further screened the key lncRNA. In vitro experiments were applied to validate the prediction. Treg is the key immune cell in the TME of lung adenocarcinoma, and the degree of infiltration is negatively correlated with the prognosis. PCBP1-AS1 may affect the infiltration of Tregs by regulating the TGF-β pathway, which is a potential predictor of clinical response to immunotherapy. PCBP1-AS1 regulates cell proliferation, cell cycle, invasion, migration, and apoptosis in lung adenocarcinoma. The results of clinical sample staining and in vitro experiments showed that PCBP1-AS1 was negatively correlated with Treg infiltration and TGF-β expression. Tregs and related lncRNA PCBP1-AS1 can be used as targets for the diagnosis and treatment of lung adenocarcinoma.

Novel lncRNAs LINC01221, RP11-472G21.2 and CRNDE are markers of differential expression in pediatric patients with T cell acute lymphoblastic leukemia

INTRODUCTION

Pediatric T-cell acute lymphoblastic leukemia (T-ALL) poses significant challenges due to its aggressive nature and resistance to standard treatments. Long non-coding RNAs (lncRNAs) have emerged as potential biomarkers and therapeutic targets in leukemia. This study aims to characterize the lncRNA landscape in pediatric T-ALL, identify specific lncRNAs signatures, and assess their clinical relevance.

METHODS

RNA sequencing was performed on T-ALL patient and control samples. Differential expression analysis identified dysregulated lncRNAs and mRNAs. Functional enrichment analysis revealed potential roles of these lncRNAs in cancer pathogenesis. Validation of candidate lncRNAs was conducted using real-time PCR. Clinical correlations were assessed, including associations with patients' clinical characteristics and survival outcomes.

RESULTS

Analysis identified 674 dysregulated lncRNAs in pediatric T-ALL, with LINC01221 and CRNDE showing the most interactions in cancer progression pathways. Functional enrichment indicated involvement in apoptosis, survival, proliferation, and metastasis. Top 10 lncRNAs based on adjusted p value < 0.05 and Fold Change > 2 were selected for validation. Seven lncRNAs LINC01221, PCAT18, LINC00977, RP11-620J15.3, RP11-472G21.2, CTD-2291D10.4, and CRNDE showed correlation with RNA sequencing data. RP11-472G21.2 and CTD-2291D10.4 were highly expressed in T-ALL patients, with RP11-620J15.3 correlating significantly with better overall survival (p = 0.0007) at a median follow up of 32 months. The identified lncRNAs were further analysed in B-ALL patients. Distinct lncRNAs signatures were noted, distinguishing T-ALL from B-ALL and healthy controls, with lineage-specific overexpression of LINC01221 (p < 0.0001), RP11-472G21.2 (p < 0.001) and CRNDE (p = 0.04) in T-ALL.

CONCLUSION

This study provides insights into the lncRNA landscape of pediatric T-ALL, offering potential diagnostic and prognostic markers. RP11-620J15.3 emerges as a promising prognostic marker, and distinct lncRNAs signatures may aid in the differentiation of T-ALL subtypes. Further research with larger cohorts is warranted to validate these findings and advance personalized treatment strategies for pediatric T-ALL patients.

Identification of lncRNA and weighted gene coexpression network analysis of germinating Rhizopus delemar causing mucormycosis

Rhizopus delemar, an opportunistic fungal pathogen, causes a highly fatal disease, mucormycosis. Spore germination is a crucial mechanism for disease pathogenesis. Thus, exploring the molecular mechanisms of fungal germination would underpin our knowledge of such transformation and, in turn, help control mucormycosis. To gain insight into the developmental process particularly associated with cell wall modification and synthesis, weighted gene co-expression network analysis (WGCNA) was performed including both coding and non-coding transcripts identified in the current study, to find out the module of interest in the germination stages. The module-trait relationship identified a particular module to have a high correlation only at the resting phase and further analysis revealed the module to be enriched for protein phosphorylation, carbohydrate metabolic process, and cellular response to stimulus. Moreover, co-expression network analysis of highly connected nodes revealed cell wall modifying enzymes, especially those involved in mannosylation, chitin-glucan crosslinking, and polygalacturonase activities co-expressing and interacting with the novel lncRNAs among which some of them predicted to be endogenous target mimic (eTM) lncRNAs. Hence, the present study provides an insight into the onset of spore germination and the information on the novel non-coding transcripts with key cell wall-related enzymes as potential targets against mucormycosis.

NOCICEPTRA2.0 - A comprehensive ncRNA atlas of human native and iPSC-derived sensory neurons

Non-coding RNAs (ncRNAs) are pivotal in gene regulation during development and disease. MicroRNAs have been extensively studied in neurogenesis. However, limited knowledge exists about the developmental signatures of other ncRNA species in sensory neuron differentiation, and human dorsal root ganglia (DRG) ncRNA expression remains undocumented. To address this gap, we generated a comprehensive atlas of small ncRNA species during iPSC-derived sensory neuron differentiation. Utilizing iPSC-derived sensory neurons and human DRG RNA sequencing, we unveiled signatures describing developmental processes. Our analysis identified ncRNAs associated with various sensory neuron stages. Striking similarities in ncRNA expression signatures between human DRG and iPSC-derived neurons support the latter as a model to bridge the translational gap between preclinical findings and human disorders. In summary, our research sheds light on the role of ncRNA species in human nociceptors, and NOCICEPTRA2.0 offers a comprehensive ncRNA database for sensory neurons that researchers can use to explore ncRNA regulators in nociceptors thoroughly.

System biology analysis to develop diagnostic biomarkers, monitoring pathological indexes, and novel therapeutic approaches for immune targeting based on maggot bioactive compounds and polyphenolic cocktails in mice with gastric cancer

Early diagnosis and prognosis are prerequisites for mitigating mortality in gastric cancer (GaCa). Identifying some causative or sensitive elements (coding RNA (cRNA)-non-cRNAs (ncRNAs)) can be very helpful in the early diagnosis of GaCa. Notably, despite significant development in the GaCa treatment, the outcome of patients does not remain satisfactory due to limitations such as multi-drug resistance and tumor relapse. Therefore, more attention has been drawn to complementary therapies and the use of supplements. In this regard, Polyphenol natural compounds (PNC) and maggot larvae (MaLa) alone or in combination were administered along with chemotherapy (paclitaxel) to N-methyl-N-nitrosourea (MNU)- induced murine tumor model. In addition, in order to identify potential diagnostic or prognostic biomarkers, transcriptomics analysis was performed through a bioinformatics approach. Then transcription profile of ncRNAs with their target hub genes was assessed through qPCR Real-Time, Western blot, and ELISA. According to the bioinformatics results, 17 hub genes (e.g., IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, and IFN-γ) were explored that contribute towards inflammation and oxidative stress and ultimately GaCa development. Upstream of the mentioned hub genes, regulatory factors (lncRNA XIST and NEAT1) were also identified and introduced as prognosis and diagnosis biomarkers for GaCa. Our results showed that PNC alone and in combination with MaLa was able to reduce the size and number of tumors, which is related to the reduction of genes expression levels (including IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, IFN-γ, NEAT1, and XIST). In conclusion, PNC and MaLa have the potential to be considered as complementary and improving chemotherapy due to their effective compounds. Also, the introduced hub gene and lncRNA in addition to diagnostic and prognostic biomarkers can be used as druggable proteins for novel therapeutic targeting of GaCa.

Transcriptomes of human primary skin fibroblasts of healthy individuals reveal age-associated mRNAs and long noncoding RNAs

Changes in the transcriptomes of human tissues with advancing age are poorly cataloged. Here, we sought to identify the coding and long noncoding RNAs present in cultured primary skin fibroblasts collected from 82 healthy individuals across a wide age spectrum (22-89 years old) who participated in the GESTALT (Genetic and Epigenetic Signatures of Translational Aging Laboratory Testing) study of the National Institute on Aging, NIH. Using high-throughput RNA sequencing and a linear regression model, we identified 1437 coding RNAs (mRNAs) and 1177 linear and circular long noncoding (lncRNAs) that were differentially abundant as a function of age. Gene set enrichment analysis (GSEA) revealed select transcription factors implicated in coordinating the transcription of subsets of differentially abundant mRNAs, while long noncoding RNA enrichment analysis (LncSEA) identified RNA-binding proteins predicted to participate in the age-associated lncRNA profiles. In summary, we report age-associated changes in the global transcriptome, coding and noncoding, from healthy human skin fibroblasts and propose that these transcripts may serve as biomarkers and therapeutic targets in aging skin.

Long noncoding RNA study: Genome-wide approaches

Long noncoding RNAs (lncRNAs) have been confirmed to play a crucial role in various biological processes across several species. Though many efforts have been devoted to the expansion of the lncRNAs landscape, much about lncRNAs is still unknown due to their great complexity. The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases. In this review, we introduced genome-wide research of lncRNAs in three parts: (i) novel lncRNA identification by high-throughput sequencing and computational pipelines; (ii) functional characterization of lncRNAs by expression atlas profiling, genome-scale screening, and the research of cancer-related lncRNAs; (iii) mechanism research by large-scale experimental technologies and computational analysis. Besides, primary experimental methods and bioinformatic pipelines related to these three parts are summarized. This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.

LncRNA expression signature identified using genome-wide transcriptomic profiling to predict lymph node metastasis in patients with stage T1 and T2 gastric cancer

BACKGROUND

Lymph node (LN) status is vital to evaluate the curative potential of relatively early gastric cancer (GC; T1-T2) treatment (endoscopic or surgery). Currently, there is a lack of robust and convenient methods to identify LN metastasis before therapeutic decision-making.

METHODS

Genome-wide expression profiles of long noncoding RNA (lncRNA) in primary T1 gastric cancer data from The Cancer Genome Atlas (TCGA) was used to identify lncRNA expression signature capable of detecting LN metastasis of GC and establish a 10-lncRNA risk-prediction model based on deep learning. The performance of the lncRNA panel in diagnosing LN metastasis was evaluated both in silico and clinical validation methods. In silico validation was conducted using TCGA and Asian Cancer Research Group (ACRG) datasets. Clinical validation was performed on T1 and T2 patients, and the panel's efficacy was compared with that of traditional tumor markers and computed tomography (CT) scans.

RESULTS

Profiling of genome-wide RNA expression identified a panel of lncRNA to predict LN metastasis in T1 stage gastric cancer (AUC = 0.961). A 10-lncRNA risk-prediction model was then constructed, which was validated successfully in T1 and T2 datasets (TCGA, AUC = 0.852; ACRG, AUC = 0.834). Thereafter, the clinical performance of the lncRNA panel was validated in clinical cohorts (T1, AUC = 0.812; T2, AUC = 0.805; T1 + T2, AUC = 0.764). Notably, the panel demonstrated significantly better performance compared with CT and traditional tumor markers.

CONCLUSIONS

The novel 10-lncRNA could diagnose LN metastasis robustly in relatively early gastric cancer (T1-T2), with promising clinical potential.

Long non-coding RNA SNHG8 drives stress granule formation in tauopathies

Tauopathies are a heterogenous group of neurodegenerative disorders characterized by tau aggregation in the brain. In a subset of tauopathies, rare mutations in the MAPT gene, which encodes the tau protein, are sufficient to cause disease; however, the events downstream of MAPT mutations are poorly understood. Here, we investigate the role of long non-coding RNAs (lncRNAs), transcripts >200 nucleotides with low/no coding potential that regulate transcription and translation, and their role in tauopathy. Using stem cell derived neurons from patients carrying a MAPT p.P301L, IVS10 + 16, or p.R406W mutation and CRISPR-corrected isogenic controls, we identified transcriptomic changes that occur as a function of the MAPT mutant allele. We identified 15 lncRNAs that were commonly differentially expressed across the three MAPT mutations. The commonly differentially expressed lncRNAs interact with RNA-binding proteins that regulate stress granule formation. Among these lncRNAs, SNHG8 was significantly reduced in a mouse model of tauopathy and in FTLD-tau, progressive supranuclear palsy, and Alzheimer's disease brains. We show that SNHG8 interacts with tau and stress granule-associated RNA-binding protein TIA1. Overexpression of mutant tau in vitro is sufficient to reduce SNHG8 expression and induce stress granule formation. Rescuing SNHG8 expression leads to reduced stress granule formation and reduced TIA1 levels in immortalized cells and in MAPT mutant neurons, suggesting that dysregulation of this non-coding RNA is a causal factor driving stress granule formation via TIA1 in tauopathies.

Identification of urinary extracellular vesicles differentially expressed RNAs in diabetic nephropathy via whole-transcriptome integrated analysis

BACKGROUND

Diabetic nephropathy (DN) is a common systemic microvascular complication of diabetes and a leading cause of chronic kidney disease worldwide. Urinary extracellular vesicles (uEVs), which are natural nanoscale vesicles that protect RNA from degradation, have the potential to serve as an invasive diagnostic biomarker for DN.

METHODS

We enrolled 24 participants, including twelve with renal biopsy-proven T2DN and twelve with T2DM, and isolated uEVs using ultracentrifugation. We performed microarrays for mRNAs, lncRNAs, and circRNAs in parallel, and Next-Generation Sequencing for miRNAs. Differentially expressed RNAs (DE-RNAs) were subjected to CIBERSORTx, ssGSEA analysis, GO enrichment, PPI network analysis, and construction of the lncRNA/circRNA-miRNA-mRNA regulatory network. Candidate genes and potential biomarker RNAs were validated using databases and machine learning models.

RESULTS

A total of 1684 mRNAs, 126 lncRNAs, 123 circRNAs and 66 miRNAs were found in uEVs in T2DN samples compared with T2DM. CIBERSORTx revealed the involvement of uEVs in immune activity and ssGSEA explored possible cell or tissue sources of uEVs. A ceRNA co-expression and regulation relationship network was constructed. Candidate genes MYO1C and SP100 mRNA were confirmed to be expressed in the kidney using Nephroseq database, scRNA-seq dataset, and Human Protein Atlas database. We further selected 2 circRNAs, 2 miRNAs, and 2 lncRNAs from WGCNAs and ceRNAs and demonstrated their efficacy as potential diagnostic biomarkers for T2DN using machine learning algorithms.

CONCLUSIONS

This study reported, for the first time, the whole-transcriptome genetic resources found in urine extracellular vesicles of T2DN patients. The results provide additional support for the possible interactions, and regulators between RNAs from uEVs themselves and as potential biomarkers in DN.

Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers

Non-coding RNAs (ncRNAs) act as important modulators of gene expression and they have been confirmed to play critical roles in the physiology and development of malignant tumors. Understanding the synergism of multiple ncRNAs in competing endogenous RNA (ceRNA) regulation can provide important insights into the mechanisms of malignant tumors caused by ncRNA regulation. In this work, we present a framework, SCOM, for identifying ncRNA synergistic competition. We systematically construct the landscape of ncRNA synergistic competition across 31 malignant tumors, and reveal that malignant tumors tend to share hub ncRNAs rather than the ncRNA interactions involved in the synergistic competition. In addition, the synergistic competition ncRNAs (i.e. ncRNAs involved in the synergistic competition) are likely to be involved in drug resistance, contribute to distinguishing molecular subtypes of malignant tumors, and participate in immune regulation. Furthermore, SCOM can help to infer ncRNA synergistic competition across malignant tumors and uncover potential diagnostic and prognostic biomarkers of malignant tumors. Altogether, the SCOM framework (https://github.com/zhangjunpeng411/SCOM/) and the resulting web-based database SCOMdb (https://comblab.cn/SCOMdb/) serve as a useful resource for exploring ncRNA regulation and to accelerate the identification of carcinogenic biomarkers.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.

A New Understanding of Long Non-Coding RNA in Hepatocellular Carcinoma-From m6A Modification to Blood Biomarkers

With recent advancements in biological research, long non-coding RNAs (lncRNAs) with lengths exceeding 200 nucleotides have emerged as pivotal regulators of gene expression and cellular phenotypic modulation. Despite initial skepticism due to their low sequence conservation and expression levels, their significance in various biological processes has become increasingly apparent. We provided an overview of lncRNAs and discussed their defining features and modes of operation. We then explored their crucial function in the hepatocarcinogenesis process, elucidating their complex involvement in hepatocellular carcinoma (HCC). The influential role of lncRNAs within the HCC tumor microenvironment is emphasized, illustrating their potential as key modulators of disease dynamics. We also investigated the significant influence of N6-methyladenosine (m6A) modification on lncRNA function in HCC, enhancing our understanding of both their roles and their upstream regulators. Additionally, the potential of lncRNAs as promising biomarkers was discussed in liver cancer diagnosis, suggesting a novel avenue for future research and clinical application. Finally, our work underscored the dual potential of lncRNAs as both contributors to HCC pathogenesis and innovative tools for its diagnosis. Existing challenges and prospective trajectories in lncRNA research are also discussed, emphasizing their potential in advancing liver cancer research.

The utility of long non-coding RNAs in chronic obstructive pulmonary disease: a comprehensive analysis

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is one of the main causes of morbidity and mortality in the world. However, there are some patients who are not diagnosed early and correctly through routine methods because of inconspicuous or serious symptoms. This study aims to assess the diagnostic role of long non-coding RNA (lncRNA) in COPD.

METHODS

We searched literature from electronic databases, after excluding non-COPD literature, the bibliometric analysis was performed, and VOSviewer software was used to represent the data analyzed. Literature evaluating the diagnostic test accuracy of lncRNA for COPD was eligible, and the QUADAS-2 checklist was used to evaluate the quality. The pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratio (DOR), and summary receiver operating characteristic curve (sROC) were used to analyze the overall diagnostic performance. Subgroup and meta-regression analyses were performed to explore the heterogeneity, and a funnel plot was assessed for publication bias. Also, lncRNAs related to COPD were identified and explored for their potential biological function.

RESULTS

An increased annual growth rate of literature on this subject from 2016 focused on COPD, humans, RNA, and lncRNA. The meta-analysis enrolled 17 literature indicated that the SEN, SPE, and DOR differentiating COPD patients from normal controls (NCs) were 0.86 (95% CI [0.80, 0.90]), 0.78 (95% CI [0.67, 0.86]), and 21.59 (95% CI [11.39, 40.91]), respectively. Meanwhile, lncRNAs had the ability to distinguish acute exacerbations of COPD (AECOPD) patients from COPD; the SEN, SPE, and DOR were 0.75 (95% CI [0.62, 0.85]), 0.81 (95% CI [0.71, 0.89]), and 13.02 (95% CI [7.76, 21.85]), respectively. The area under the sROC were calculated to be greater than 0.8 at least. Subgroup and meta-regression analysis showed that the types of specimens and dysregulated lncRNAs might affect the diagnostic accuracy. The funnel plot showed there was a certain publication bias. 41 lncRNAs related to COPD were identified and mainly located in the nucleus and cytoplasm, associated with proliferation, invasion, and prognosis. These lncRNA-binding proteins were involved in the spliceosome, Rap1 signaling pathway, MAPK signaling pathway, and so on.

CONCLUSION

LncRNA suggests potential diagnostic biomarkers and therapeutic targets for COPD patients.

The Landscape of m6A Regulators in Multiple Brain Regions of Alzheimer's Disease

Alzheimer's disease research has been conducted for many years, yet no effective cure methods have been found. N6-methyladenosine (m6A) RNA methylation, an essential post-transcriptional regulation mechanism, has been discovered to affect essential neurobiological processes, such as brain cell development and aging, which are closely related to neurodegenerative diseases such as Alzheimer's disease. The relationship between Alzheimer's disease and the m6A mechanism still needs further investigation. Our work evaluated the alteration profile of m6A regulators and their influences on Alzheimer's disease in 4 brain regions: the postcentral gyrus, superior frontal gyrus, hippocampus, and entorhinal cortex. We found that the expression levels of the m6A regulators FTO, ELAVL1, and YTHDF2 were altered in Alzheimer's disease and were related to pathological development and cognitive levels. We also assessed AD-related biological processes influenced by m6A regulators via GSEA and GSVA method. Biological Processes Gene Ontology terms including memory, cognition, and synapse-signaling were found to potentially be affected by m6A regulators in AD. We also found different m6A modification patterns in AD samples among different brain regions, mainly due to differences in m6A readers. Finally, we further evaluated the importance of AD-related regulators based on the WGCNA method, assessed their potential targets based on correlation relationships, and constructed diagnostic models in 3 of all 4 regions using hub regulators, including FTO, YTHDC1, YTHDC2, etc., and their potential targets. This work aims to provide a reference for the follow-up study of m6A and Alzheimer's disease.

In Silico Bioinformatics Analysis on the Role of Long Non-Coding RNAs as Drivers and Gatekeepers of Androgen-Independent Prostate Cancer Using LNCaP and PC-3 Cells

Prostate cancer (PCa) is the leading cancer in men globally. The association between PCa and long non-coding RNAs (lncRNAs) has been reported. Aberrantly expressed lncRNAs have been documented in each of the cancer "hallmarks". Androgen signaling plays an important role in PCa progression. This study aimed to profile the aberrantly expressed lncRNAs in androgen-dependent (LNCaP) PCa compared to androgen-independent (PC-3) PCa cells. This was achieved by using a 384-well plate of PCa lncRNA gene panel. Differential expression of ±2 up or downregulation was determined using the CFX Maestro software v2.1. LncSEA and DIANA-miRPath were used to identify the enriched pathways. Telomerase RNA component (TERC) lncRNA was illustrated to participate in various tumourigenic classes by in silico bioinformatics analysis and was thus selected for validation using RT-qPCR. Further bioinformatics analysis revealed the involvement of differentially expressed lncRNAs in oncogenic pathways. Some lncRNAs undergo hypermethylation, others are encapsulated by exosomes, while others interact with several microRNAs (miRNAs), favouring tumourigenic pathways. Notably, TERC lncRNA was shown to interact with tumour-suppressor miRNAs hsa-miR-4429 and hsa-miR-320b. This interaction in turn activates TGF-β-signaling and ECM-receptor interaction pathways, favouring the progression of PCa. Understanding lncRNAs as competitive endogenous RNA molecules and their interactions with miRNAs may aid in the identification of novel prognostic PCa biomarkers and therapeutic targets.

Comprehensive analysis of circulating cell-free RNAs in blood for diagnosing non-small cell lung cancer

Early screening and detection of non-small cell lung cancer (NSCLC) is crucial due to the significantly low survival rate in advanced stages. Blood-based liquid biopsy is non-invasive test to assistant disease diagnosis, while cell-free RNA is one of the promising biomarkers in blood. However, the disease related signatures have not been explored completely for most cell-free RNA transcriptome sequencing (cfRNA-Seq) datasets. To address this gap, we developed a comprehensive cfRNA-Seq pipeline for data analysis and constructed a machine learning model to facilitate noninvasive early diagnosis of NSCLC. The results of our study have demonstrated the identification of differential mRNA, lncRNAs and miRNAs from cfRNA-Seq, which have exhibited significant association with development and progression of lung cancer. The classifier based on gene expression signatures achieved an impressive area under the curve (AUC) of up to 0.9, indicating high specificity and sensitivity in both cross-validation and independent test. Furthermore, the analysis of T cell and B cell immune repertoire extracted from cfRNA-Seq have provided insights into the immune status of cancer patients, while the microbiome analysis has revealed distinct bacterial and viral profiles between NSCLC and normal samples. In our future work, we aim to validate the existence of cancer associated T cell receptors (TCR)/B cell receptors (BCR) and microorganisms, and subsequently integrate all identified signatures into diagnostic model to improve the prediction accuracy. This study not only provided a comprehensive analysis pipeline for cfRNA-Seq dataset but also highlights the potential of cfRNAs as promising biomarkers and models for early NSCLC diagnosis, emphasizing their importance in clinical settings.

Pan-cancer survey of lncRNA rewiring and functional alternation in tumor-infiltrating T cell by scLNC

Long non-coding RNAs (lncRNAs) have been reported to involve in diverse biological processes, including tumor immunity. Since lncRNAs are expressed with high cell-type specificity, investigation of lncRNAs at the single-cell level will unveil the cell-type-specific functions of lncRNAs. However, at the single-cell level, a systematic pan-cancer analysis of lncRNA functions in tumor immune microenvironments (TIMEs) remains lacking. Here, we performed pan-cancer single-cell profiling of lncRNA functions in TIMEs and developed a tool, scLNC, tailored for lncRNA functional characterization at the single-cell level. scLNC enabled the comparison of lncRNA function from the levels of lncRNA-mRNA pairs, lncRNA regulatory unit activity and unit function in a cell-type-specific manner. Applying scLNC, our analysis depicted the cross-tumor and tumor-specific lncRNA regulatory profiles in the T cell subtypes and revealed the new regulatory units that lncRNAs established in tumor-infiltrating T cells, particularly in the tumor-enriched T cells. We further characterized the activity and functional alternations of lncRNAs through their regulatory units. Overall, our findings suggested that lncRNAs played an important role in the regulation of cytokine production, cell activation and migration in tumor-enriched T cells and further in immunotherapy.

Cuproptosis-related lncRNAs predict prognosis and immune response of thyroid carcinoma

Objective: To estimate the survival and prognosis of patients with thyroid carcinoma (THCA) based on the Long non-coding RNA (lncRNA) traits linked to cuproptosis and to investigate the connection between the immunological spectrum of THCA and medication sensitivity. Methods: RNA-Seq data and clinical information for THCA were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We built a risk prognosis model by identifying and excluding lncRNAs associated with cuproptosis using Cox regression and LASSO methods. Both possible biological and immune infiltration functions were investigated using Principal Component Analysis (PCA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and immunoassays. The sensitivity of the immune response to possible THCA medicines was assessed using ratings for tumor immune dysfunction and exclusion (TIDE) and tumor mutational burden (TMB). Results: Seven cuproptosis-related lncRNAs were used to construct our prognostic prediction model: AC108704.1, DIO3OS, AL157388.1, AL138767.3, STARD13-AS, AC008532.1, and PLBD1-AS1. Using data from TCGA's training, testing, and all groups, Kaplan-Meier and ROC curves demonstrated this feature's adequate predictive validity. Different clinical characteristics have varying effects on cuproptosis-related lncRNA risk models. Further analysis of immune cell infiltration and single sample Gene Set Enrichment Analysis (ssGSEA) supported the possibility that cuproptosis-associated lncRNAs and THCA tumor immunity were closely connected. Significantly, individuals with THCA showed a considerable decline in survival owing to the superposition effect of patients in the high-risk category and high TMB. Additionally, the low-risk group had a higher TIDE score compared with the high-risk group, indicating that these patients had suboptimal immune checkpoint blocking responses. To ensure the accuracy and reliability of our results, we further verified them using several GEO databases. Conclusion: The clinical and risk aspects of cuproptosis-related lncRNAs may aid in determining the prognosis of patients with THCA and improving therapeutic choices.

5-Methylcytosine-related lncRNAs: predicting prognosis and identifying hot and cold tumor subtypes in head and neck squamous cell carcinoma

BACKGROUND

5-Methylcytosine (m⁵C) methylation is recognized as an mRNA modification that participates in biological progression by regulating related lncRNAs. In this research, we explored the relationship between m⁵C-related lncRNAs (mrlncRNAs) and head and neck squamous cell carcinoma (HNSCC) to establish a predictive model.

METHODS

RNA sequencing and related information were obtained from the TCGA database, and patients were divided into two sets to establish and verify the risk model while identifying prognostic mrlncRNAs. Areas under the ROC curves were assessed to evaluate the predictive effectiveness, and a predictive nomogram was constructed for further prediction. Subsequently, the tumor mutation burden (TMB), stemness, functional enrichment analysis, tumor microenvironment, and immunotherapeutic and chemotherapeutic responses were also assessed based on this novel risk model. Moreover, patients were regrouped into subtypes according to the expression of model mrlncRNAs.

RESULTS

Assessed by the predictive risk model, patients were distinguished into the low-MLRS and high-MLRS groups, showing satisfactory predictive effects with AUCs of 0.673, 0.712, and 0.681 for the ROCs, respectively. Patients in the low-MLRS groups exhibited better survival status, lower mutated frequency, and lower stemness but were more sensitive to immunotherapeutic response, whereas the high-MLRS group appeared to have higher sensitivity to chemotherapy. Subsequently, patients were regrouped into two clusters: cluster 1 displayed immunosuppressive status, but cluster 2 behaved as a hot tumor with a better immunotherapeutic response.

CONCLUSIONS

Referring to the above results, we established a m⁵C-related lncRNA model to evaluate the prognosis, TME, TMB, and clinical treatments for HNSCC patients. This novel assessment system is able to precisely predict the patients' prognosis and identify hot and cold tumor subtypes clearly for HNSCC patients, providing ideas for clinical treatment.

Bioinformatics identification and validation of aging-related molecular subtype and prognostic signature in sarcoma

Aging could regulate many biological processes in malignancies by regulating cell senescence. Consensus cluster analysis was conducted to differentiate TCGA sarcoma cases. LASSO cox regression analysis was performed to construct an aging-related prognostic signature. We identified two categories of TCGA-sarcoma with significant difference in prognosis, immune infiltration and chemotherapy and targeted therapy. Moreover, an aging-related prognostic signature was constructed for sarcoma, which had a good performance in predicting the 3-year and 5-year overall survival of sarcoma patients. We also identified a lncRNA MALAT1/miR-508-3p/CCNA2 regulatory axis for sarcoma. This stratification could provide more evidence for estimating prognosis and immunotherapy of sarcoma.

Downregulation of lncRNA SNHG1 in hypoxia and stem cells is associated with poor disease prognosis in gliomas

Gliomas are brain tumors associated with high morbidity, relapse and lethality despite improvement in therapeutic regimes. The hypoxic tumor microenvironment is a key feature associated with such poor outcomes in gliomas. The Hypoxia Inducible Factor (HIF) family of transcription factors are master regulators of cellular proliferation, high metabolic rates and angiogenesis via aberrant expression of downstream genes. Recent studies have implicated long non-coding RNAs (lncRNAs) as potential prognostic and diagnostic biomarkers. In this study, identification of hypoxia regulated lncRNA with a bioinformatic pipeline consisting of a newly developed tool "GenOx" was utilized for the identification of Hypoxia Response Element (HRE) and Hypoxia Ancillary Sequence (HAS) motifs in the promoter regions of lncRNAs. This was coupled with molecular, functional and interactome-based analyses of these hypoxia-relevant lncRNAs in primary tumors and cell-line models. We report on the identification of novel hypoxia regulated lncRNAs SNHG12, CASC7 and MF12-AS1. A strong association of RNA splicing mechanisms was observed with enriched lncRNAs. Several lncRNAs have emerged as prognostic biomarkers, of which TP53TG1 and SNHG1 were identified as highly relevant lncRNAs in glioma progression and validated in hypoxia cultured cells. Significantly, we determined that SNHG1 expression in tumor (vs. normal) is different from glioma stem cells, GSC (vs. tumors) and in hypoxia (vs. normoxia), positioning downregulation of SNHG1 to be associated with worsened prognosis.

Pan-cancer analysis of super enhancer-induced PRR7-AS1 as a potential prognostic and immunological biomarker

Introduction: Systematic pan-cancer analysis of the roles and regulatory mechanisms for PRR7-AS1 is currently not available. Methods: In the present study, a comprehensive bioinformatic approach was used to mine the underlying oncogenic effects of PRR7-AS1, including expression status, prognostic value and immune characteristics. Results: We discovered that PRR7-AS1 expression was remarkably upregulated in most cancer types and exhibited a negative correlation with the prognosis. Furthermore, PRR7-AS1 expression was inversely connected with the majority of tumor-infiltrating immune cells, immune scores and immune checkpoint gene expression in pancancer. There was also a significant correlation between PRR7-AS1 expression status and tumor mutational burden, microsatellite instability, and neoantigens in certain tumors. PRR7-AS1 had the best predictive power for immune checkpoint blockade efficacy compared to other well-recognized biomarkers. PRR7-AS1 overexpression could affect cytotoxic T cells-mediated antitumor responses. Functional enrichment analysis revealed that PRR7-AS1 might be involved in the metabolic pathways. Super enhancer activity might have participated in the regulation of PRR7-AS1 expression. And we constructed the competitive endogenous RNA networks for PRR7-AS1. Discussion: In general, PRR7-AS1 had the potential to be a diagnostic, prognostic and immune biomarker for pan cancer. PRR7-AS1 was correlated with an immunosuppressive microenvironment and was a new potential target for immunotherapy. Epigenetic factors were the driving forces for PRR7-AS1 overexpression in tumors.

Rewiring of the 3D genome during acquisition of carboplatin resistance in a triple-negative breast cancer patient-derived xenograft

Changes in the three-dimensional (3D) structure of the genome are an emerging hallmark of cancer. Cancer-associated copy number variants and single nucleotide polymorphisms promote rewiring of chromatin loops, disruption of topologically associating domains (TADs), active/inactive chromatin state switching, leading to oncogene expression and silencing of tumor suppressors. However, little is known about 3D changes during cancer progression to a chemotherapy-resistant state. We integrated chromatin conformation capture (Hi-C), RNA-seq, and whole-genome sequencing obtained from triple-negative breast cancer patient-derived xenograft primary tumors (UCD52) and carboplatin-resistant samples and found increased short-range (< 2 Mb) interactions, chromatin looping, formation of TAD, chromatin state switching into a more active state, and amplification of ATP-binding cassette transporters. Transcriptome changes suggested the role of long-noncoding RNAs in carboplatin resistance. Rewiring of the 3D genome was associated with TP53, TP63, BATF, FOS-JUN family of transcription factors and led to activation of aggressiveness-, metastasis- and other cancer-related pathways. Integrative analysis highlighted increased ribosome biogenesis and oxidative phosphorylation, suggesting the role of mitochondrial energy metabolism. Our results suggest that 3D genome remodeling may be a key mechanism underlying carboplatin resistance.

Roles of lncRNAs in pancreatic ductal adenocarcinoma: Diagnosis, treatment, and the development of drug resistance

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, primarily due to its late diagnosis, high propensity to metastasis, and the development of resistance to chemo-/radiotherapy. Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are intimately involved in the treatment resistance of pancreatic cancer cells via interacting with critical signaling pathways and may serve as potential diagnostic/prognostic markers or therapeutic targets in PDAC.

DATA SOURCES

We carried out a systematic review on lncRNAs-based research in the context of pancreatic cancer and presented an overview of the updated information regarding the molecular mechanisms underlying lncRNAs-modulated pancreatic cancer progression and drug resistance, together with their potential value in diagnosis, prognosis, and treatment of PDAC. Literature mining was performed in PubMed with the following keywords: long non-coding RNA, pancreatic ductal adenocarcinoma, pancreatic cancer up to January 2022. Publications relevant to the roles of lncRNAs in diagnosis, prognosis, drug resistance, and therapy of PDAC were collected and systematically reviewed.

RESULTS

LncRNAs, such as HOTAIR, HOTTIP, and PVT1, play essential roles in regulating pancreatic cancer cell proliferation, invasion, migration, and drug resistance, thus may serve as potential diagnostic/prognostic markers or therapeutic targets in PDAC. They participate in tumorigenesis mainly by targeting miRNAs, interacting with signaling molecules, and involving in the epithelial-mesenchymal transition process.

CONCLUSIONS

The functional lncRNAs play essential roles in pancreatic cancer cell proliferation, invasion, migration, and drug resistance and have potential values in diagnosis, prognostic prediction, and treatment of PDAC.

Incidence and risk of hypertension associated with PARP inhibitors in cancer patients: a systematic review and meta-analysis

OBJECTIVE

To analyze the incidence and risk of hypertension associated with poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitors in cancer patients and provide reference for clinicians.

METHODS

We used R software to conduct a meta-analysis of phase II/III randomized controlled trials (RCT) on PARP inhibitors for cancer treatment published in PubMed, Embase, Clinical Trials, Cochrane Library and Web of Science from inception to July 29th, 2022.

RESULTS

We included 32 RCTs with 10,654 participants for this meta-analysis. For total PARP inhibitors, the incidence and risk ratio of all-grade hypertension were 12% and 1.22 (95% CI: 0.91-1.65, P = 0.19, I² = 81%), and the incidence and risk ratio of grade 3-4 hypertension were 4% and 1.24 (95% CI: 0.74-2.08, P = 0.42, I² = 68%). Compared with the control group, the niraparib group, olaparib 800 mg/day group, and olaparib plus cediranib group increased the risk of any grade and grade 3-4 hypertension, while the veliparib group and rucaparib group did not increase the risk of any grade and grade 3-4 hypertension, and olaparib 200 mg-600 mg/day group (exclude olaparib plus cediranib regime) reduced the risk of any grade and grade 3-4 hypertension.

CONCLUSION

Olaparib 200-600 mg/day (excluding olaparib plus cediranib regimen) may be the most suitable PARP inhibitor for cancer patients with high risk of hypertension, followed by veliparib and rucaparib. Niraparib, olaparib 800 mg/day and olaparib combined with cediranib may increase the risk of developing hypertension in cancer patients, clinicians should strengthen the monitoring of blood pressure in cancer patients and give medication in severe cases.

Identification of a cellular senescence-related-lncRNA (SRlncRNA) signature to predict the overall survival of glioma patients and the tumor immune microenvironment

Background: Gliomas are brain tumors that arise from glial cells, and they are the most common primary intracranial tumors with a poor prognosis. Cellular senescence plays a critical role in cancer, especially in glioma. In this study, we constructed a senescence-related lncRNA (SRlncRNA) signature to assess the prognosis of glioma. Methods: The Cancer Genome Atlas was used to collect SRlncRNA transcriptome profiles and clinical data about glioma. Patients were randomized to training, testing, and whole cohorts. LASSO and Cox regression analyses were employed to construct the SRlncRNA signature, and Kaplan-Meier (K-M) analysis was performed to determine each cohort's survival. Receiver operating characteristic (ROC) curves were applied to verify the accuracy of this signature. Gene set enrichment analysis was used to visualize functional enrichment (GSEA). The CIBERSORT algorithm, ESTIMATE and TIMER databases were utilized to evaluate the differences in the infiltration of 22 types of immune cells and their association with the signature. RT-qPCR and IHC were used to identify the consistency of the signature in tumor tissue. Results: An SRlncRNA signature consisting of six long non-coding RNAs (lncRNAs) was constructed, and patients were divided into high-risk and low-risk groups by the median of their riskscore. The KM analysis showed that the high-risk group had worse overall survival, and the ROC curve confirmed that the riskscore had more accurate predictive power. A multivariate Cox analysis and its scatter plot with clinical characteristics confirmed the riskscore as an independent risk factor for overall survival. GSEA showed that the GO and KEGG pathways were mainly enriched in the immune response to tumor cells, p53 signaling pathway, mTOR signaling pathway, and Wnt signaling pathway. Further validation also yielded significant differences in the risk signature in terms of immune cell infiltration, which may be closely related to prognostic differences, and qRT-PCR and IHC confirmed the consistency of the expression differences in the major lncRNAs with those in the prediction model. Conclusion Our findings indicated that the SRlncRNA signature might be used as a predictive biomarker and that there is a link between it and immune infiltration. This discovery is consistent with the present categorization system and may open new avenues for research and personalized therapy.

Identification of non-coding RNA related prognosis biomarkers based on ceRNA network in thyroid cancer

Introduction: Thyroid cancer (THCA) has become a serious malignant tumor worldwide. Identification of non-coding RNA related regulators is very necessary to improve the knowledge of THCA treatment. The aim of this study was to identify novel therapeutic targets and prognosis biomarkers for predicting pathological characteristics and subsequently treating THCA. Methods: We investigated the alterations of miRNAs, mRNAs and lncRNAs in THCA. Functional enrichment and clustering analysis were conducted for these aberrantly expressed RNAs. Multiple interaction networks among miRNAs, mRNAs and lncRNAs were constructed and the functional modules associated with THCA patients' prognosis were identified. Furthermore, we evaluated the prognostic roles of the important miRNAs, mRNAs and lncRNAs in THCA and investigated the regulatory potential of non-coding RNAs on immune cell infiltration. Results: We firstly identified that miR-4709-3p and miR-146b-3p could significantly classify patients into high/low risk groups, which may be potential prognosis biomarkers of THCA. Secondly, we constructed a THCA-related miRNA-mRNA network, which displayed small world network topological characters. Two THCA-related functional modules were identified from the miRNA-mRNA network by MCODE. Results showed that two modules could implicate in known cancer pathways, such as apoptosis and focal adhesion. Thirdly, a THCA-related miRNA-lncRNA network was constructed. A subnetwork of miRNA-lncRNA network showed strong prognosis effect in THCA. Fourthly, we constructed a THCA-related mRNA-lncRNA network and detected several typical lncRNA-miRNA-mRNA crosstalk, such as AC068138, BCL2, miR-21 and miR-146b, which had good prognosis effect in THCA. Immune infiltration results showed that lncRNAs LA16c-329F2, RP11-395N3, RP11-423H2, RP11-399B17 and RP11-1036E20 were high related to neutrophil and dendritic cell infiltration. Discussion: Non-coding RNA-mediated gene regulatory network has the strong regulatory potential in pathological processes of THCA. All these results could help us uncover the non-coding RNA-mediated regulatory mechanism in THCA.

TLSEA: a tool for lncRNA set enrichment analysis based on multi-source heterogeneous information fusion

Long non-coding RNAs (lncRNAs) play an important regulatory role in gene transcription and post-transcriptional modification, and lncRNA regulatory dysfunction leads to a variety of complex human diseases. Hence, it might be beneficial to detect the underlying biological pathways and functional categories of genes that encode lncRNA. This can be carried out by using gene set enrichment analysis, which is a pervasive bioinformatic technique that has been widely used. However, accurately performing gene set enrichment analysis of lncRNAs remains a challenge. Most conventional enrichment analysis methods have not exhaustively included the rich association information among genes, which usually affects the regulatory functions of genes. Here, we developed a novel tool for lncRNA set enrichment analysis (TLSEA) to improve the accuracy of the gene functional enrichment analysis, which extracted the low-dimensional vectors of lncRNAs in two functional annotation networks with the graph representation learning method. A novel lncRNA-lncRNA association network was constructed by merging lncRNA-related heterogeneous information obtained from multiple sources with the different lncRNA-related similarity networks. In addition, the random walk with restart method was adopted to effectively expand the lncRNAs submitted by users according to the lncRNA-lncRNA association network of TLSEA. In addition, a case study of breast cancer was performed, which demonstrated that TLSEA could detect breast cancer more accurately than conventional tools. The TLSEA can be accessed freely at http://www.lirmed.com:5003/tlsea.

Prediction of the Prognosis of Clear Cell Renal Cell Carcinoma by Cuproptosis-Related lncRNA Signals Based on Machine Learning and Construction of ceRNA Network

Background

Clear cell renal cell carcinoma's (ccRCC) occurrence and development are strongly linked to the metabolic reprogramming of tumors, and thus far, neither its prognosis nor treatment has achieved satisfying clinical outcomes.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively, provided us with information on the RNA expression of ccRCC patients and their clinical data. Cuproptosis-related genes (CRGS) were discovered in recent massive research. With the help of log-rank testing and univariate Cox analysis, the prognostic significance of CRGS was examined. Different cuproptosis subtypes were identified using consensus clustering analysis, and GSVA was used to further investigate the likely signaling pathways between various subtypes. Univariate Cox, least absolute shrinkage and selection operator (Lasso), random forest (RF), and multivariate stepwise Cox regression analysis were used to build prognostic models. After that, the models were verified by means of the C index, Kaplan-Meier (K-M) survival curves, and time-dependent receiver operating characteristic (ROC) curves. The association between prognostic models and the tumor immune microenvironment as well as the relationship between prognostic models and immunotherapy were next examined using ssGSEA and TIDE analysis. Four online prediction websites-Mircode, MiRDB, MiRTarBase, and TargetScan-were used to build a lncRNA-miRNA-mRNA ceRNA network.

Results

By consensus clustering, two subgroups of cuproptosis were identified that represented distinct prognostic and immunological microenvironments.

Conclusion

A prognostic risk model with 13 CR-lncRNAs was developed. The immune microenvironment and responsiveness to immunotherapy are substantially connected with the model, which may reliably predict the prognosis of patients with ccRCC.

Identification of a cuproptosis-related lncRNA signature to predict the prognosis and immune landscape of head and neck squamous cell carcinoma

Background

Cuproptosis is considered a novel copper-induced cell death model regulated by targeting lipoylated TCA cycle proteins. In this study, we established a novel signature based on cuproptosis-related lncRNAs (crlncRNAs) to predict the prognosis and immune landscape of head and neck squamous cell carcinoma.

Methods

RNA-seq matrix, somatic mutation files, and clinical data were obtained from The Cancer Genome Atlas database. After dividing patients into two sets, a crlncRNA signature was established based on survival related crlncRNAs, which were selected by the univariate Cox analysis and least absolute shrinkage and selection operator Cox regression. To evaluate the model, Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) were utilized, and a nomogram was established for survival prediction. Immune landscape analysis, drug sensitivity, cluster analysis, tumor mutation burden (TMB) and ceRNA network analysis were conducted subsequently.

Results

A crlncRNA related prognosis signature was finally constructed with 12 crlncRNAs. The areas under the ROC curves (AUCs) were 0.719, 0.705 and 0.693 respectively for 1, 3, and 5-year's overall survival (OS). Patients in the low-risk group behaved a better prognosis, lower TMB, higher immune function activity and scores. In addition, patients from cluster 2 were more sensitive to chemotherapy and immunotherapy.

Conclusion

In this study, we constructed a novel crlncRNA risk model to predict the survival of HNSCC patients. This reliable and acceptable prognostic signature may guide and promote the progress of novel treatment strategies for HNSCC patients.

Notch signaling pathway: a comprehensive prognostic and gene expression profile analysis in breast cancer

Breast cancer is a complex disease exhibiting a great degree of heterogeneity due to different molecular subtypes. Notch signaling regulates the differentiation of breast epithelial cells during normal development and plays a crucial role in breast cancer progression through the abnormal expression of the Notch up-and down-stream effectors. To date, there are only a few patient-centered clinical studies using datasets characterizing the role of Notch signaling pathway regulators in breast cancer; thus, we investigate the role and functionality of these factors in different subtypes using publicly available databases containing records from large studies. High-throughput genomic data and clinical information extracted from TCGA were analyzed. We performed Kaplan-Meier survival and differential gene expression analyses using the HALLMARK_NOTCH_SIGNALING gene set. To determine if epigenetic regulation of the Notch regulators contributes to their expression, we analyzed methylation levels of these factors using the TCGA HumanMethylation450 Array data. Notch receptors and ligands expression is generally associated with the tumor subtype, grade, and stage. Furthermore, we showed gene expression levels of most Notch factors were associated with DNA methylation rate. Modulating the expression levels of Notch receptors and effectors can be a potential therapeutic approach for breast cancer. As we outline herein, elucidating the novel prognostic and regulatory roles of Notch implicate this pathway as an essential mediator controlling breast cancer progression.

Identification and validation of the mitochondrial function related hub genes by unsupervised machine learning and multi-omics analyses in lung adenocarcinoma

Background

The mitochondrion and its associated genes were heavily implicated in developing and therapy tumors as the primary cellular organelle in charge of metabolic reprogramming and ferroptosis. Our work focuses on discovering new potential targets while analyzing the multi-omics data of mitochondria-related genes in lung adenocarcinoma (LUAD).

Methods

The Cancer Genome Atlas (TCGA) database provided multi-omics data for LUAD patients. Based on the expression profile of the genes associated with mitochondria, the patients were grouped by the unsupervised clustering method. R was used to explore the differential expressed protein-code gene, miRNA, and lncRNA, as well as their enriched functions and ceRNA networks. Additionally, the discrepancy between immune infiltration and genetic variation was comprehensively characterized. Our clinical samples and in vitro experiments investigated the hub gene determined by LASSO and batch analysis.

Results

Two clusters are distinguished using unsupervised consensus clustering based on mitochondrial heterogeneity. The integrated analysis emphasized that patients in cluster B had a worse prognosis, higher mutation frequencies, and less immune cell infiltration. The hub genes DARS2 and COX5B are identified by further analysis using LASSO penalization. In vitro experiments indicated that DARS2 and COX5B knockdown inhibited tumor cell proliferation. The specimen of our hospital cohort conducted the immunohistochemistry analysis and validated that DARS2 and COX5B's expression was significantly higher in the tumor than in adjacent normal tissue and correlated to LUAD patients' prognosis.

Conclusion

Our observations implied that LUAD patients' tumors had distinct mitochondrial function heterogeneity with different clinical and molecular characteristics. DARS2 and COX5B might be critical genes involved in mitochondrial alterations and potential therapeutic targets.

A comprehensive survey of the approaches for pathway analysis using multi-omics data integration

Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.

Prognostic analysis of m6A-related lncRNAs as potential biomarkers in intrahepatic cholangiocarcinom

Intrahepatic cholangiocarcinoma (iCCA) patients had no obvious symptoms at early stage and poor postoperative survival. Therefore, the establishment of an iCCA prognostic prediction model to carry out refined management of iCCA patients is expected to improve the survival of the iCCA patient population. In this paper, we analyzed the expression profiling data of patients from 32 iCCA tissues and eight paracancerous tissues in The Cancer Genome Atlas (TCGA) database. Perl software was used to separate M6A-related genes and lncRNAs from expression matrix files obtained from the TCGA database. The differentially expressed lncRNAs in the iCCA samples and the normal samples were screened out by differential analysis using the R package limma, and the m6A-related lncRNAs were further screened by Pearson correlation analysis. WGCNA clustering analysis constructs a random network to extract the module genes most related to iCCA, and take the intersection of differentially expressed lncRNAs related to m6A. Univariate Cox screening was performed for the intersection lncRNAs that had significant influence on the prognosis of iCCA patients, and further screening was performed by LASSO method and multivariate Cox regression analysis. Risk model was constructed and prognostic ability was evaluated according to risk score. In conclusion, we identified four m6A-related lncRNAs with potential prognostic value in iCCA, and established a novel m6A-related lncRNA-based prognostic model, which can be used as an independent prognostic factor to predict the prognosis of clinical patients.

Systematic identification of aberrant non-coding RNAs and their mediated modules in rotator cuff tears

Background: Rotator cuff tears (RCT) is the most common cause of shoulder dysfunction, however, its molecular mechanisms remain unclear. Non-coding RNAs(ncRNAs), such as long ncRNA (lncRNA), microRNA (miRNA) and circular RNA (circRNA), are involved in a variety of diseases, but little is known about their roles in RCT. Therefore, the purpose of this study is to identify dysregulated ncRNAs and understand how they influence RCT.

Methods: We performed RNA sequencing and miRNA sequencing on five pairs of torn supraspinatus muscles and matched unharmed subscapularis muscles to identify RNAs dysregulated in RCT patients. To better comprehend the fundamental biological processes, we carried out enrichment analysis of these dysregulated mRNAs or the co-expressed genes of dysregulated ncRNAs. According to the competing endogenous RNA (ceRNA) theory, we finally established ceRNA networks to explore the relationship among dysregulated RNAs in RCT.

Results: A total of 151 mRNAs, 38 miRNAs, 20 lncRNAs and 90 circRNAs were differentially expressed between torn supraspinatus muscles and matched unharmed subscapularis muscles, respectively. We found that these dysregulated mRNAs, the target mRNAs of these dysregulated miRNAs or the co-expressed mRNAs of these dysregulated ncRNAs were enriched in muscle structure development, actin-mediated cell contraction and actin binding. Then we constructed and analyzed the ceRNA network and found that the largest module in the ceRNA network was associated with vasculature development. Based on the topological properties of the largest module, we identified several important ncRNAs including hsa_circ_0000722, hsa-miR-129-5p and hsa-miR-30c-5p, whose interacting mRNAs related to muscle diseases, fat and inflammation.

Conclusion: This study presented a systematic dissection of the expression profile of mRNAs and ncRNAs in RCT patients and revealed some important ncRNAs which may contribute to the development of RCT. Such results could provide new insights for further research on RCT.

Performance evaluation of differential splicing analysis methods and splicing analytics platform construction

A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.