Novel long non-coding RNA LINC02323 promotes epithelial-mesenchymal transition and metastasis via sponging miR-1343-3p in lung adenocarcinoma

Salidroside inhibited the proliferation of gastric cancer cells through up-regulating tumor suppressor miR-1343-3p and down-regulating MAP3K6/MMP24 signal molecules

Salidroside inhibited the proliferation of cancer cell. Nevertheless, the mechanism has not been completely clarified. The purpose of the study is to explore the mechanisms of salidroside against gastric cancer. To analyze the changes of microRNA (miRNA) in gastric cancer cells under the treatment of salidroside, the miRNA expression was analyzed by using RNA-seq in cancer cells for 24 h after salidroside treatment. The differentially expressed miRNAs were clustered and their target genes were analyzed. Selected miRNA and target mRNA genes were further verified by q-PCR. The expressions of target genes in cancer cells were detected by immunohistochemistry. Cancer cell apoptotic index was significantly increased after salidroside treatment. The proliferation of gastric cancer cells were blocked at S-phase cell cycle. The expression of 44 miRNAs changed differentially after salidroside treatment in cancer cells. Bioinformatic analysis showed that there were 1384 target mRNAs corresponding to the differentially expressed miRNAs. Surprisingly, salidroside significantly up-regulated the expression of tumor suppressor miR-1343-3p, and down-regulated the expression of MAP3K6, STAT3 and MMP24-related genes. Salidroside suppressed the growth of gastric cancer by inducing the cancer cell apoptosis, arresting the cancer cell cycle and down-regulating the related signal transduction pathways. miRNAs are expressed differentially in gastric cancer cells after salidroside treatment, playing important roles in regulating proliferation and metastasis. Salidroside may suppress the growth of gastric cancer by up-regulating the expression of the tumor suppressor miR-1343-3p and down-regulating the expression of MAP3K6 and MMP24 signal molecules.

Unboxing the network among long non-coding RNAs and TGF-β signaling in cancer

Deeper analysis of molecular mechanisms arising in tumor cells is an unmet need to provide new diagnostic and therapeutic strategies to prevent and treat tumors. The transforming growth factor β (TGF-β) signaling has been steadily featured in tumor biology and linked to poor prognosis of cancer patients. One pro-tumorigenic mechanism induced by TGF-β is the epithelial-to-mesenchymal transition (EMT), which can initiate cancer dissemination, enrich the tumor stem cell population, and increase chemoresistance. TGF-β signals via SMAD proteins, ubiquitin ligases, and protein kinases and modulates the expression of protein-coding and non-coding RNA genes, including those encoding larger than 500 nt transcripts, defined as long non-coding RNAs (lncRNAs). Several reports have shown lncRNAs regulating malignant phenotypes by directly affecting epigenetic processes, transcription, and post-transcriptional regulation. Thus, this review aims to update and summarize the impact of TGF-β signaling on the expression of lncRNAs and the function of such lncRNAs as regulators of TGF-β signaling, and how these networks might impact specific hallmarks of cancer.

Comprehensive landscape and future perspective of long noncoding RNAs in non-small cell lung cancer: it takes a village

Long noncoding RNAs (lncRNAs) are a distinct subtype of RNA that lack protein-coding capacity but exert significant influence on various cellular processes. In non-small cell lung cancer (NSCLC), dysregulated lncRNAs act as either oncogenes or tumor suppressors, contributing to tumorigenesis and tumor progression. LncRNAs directly modulate gene expression, act as competitive endogenous RNAs by interacting with microRNAs or proteins, and associate with RNA binding proteins. Moreover, lncRNAs can reshape the tumor immune microenvironment and influence cellular metabolism, cancer cell stemness, and angiogenesis by engaging various signaling pathways. Notably, lncRNAs have shown great potential as diagnostic or prognostic biomarkers in liquid biopsies and therapeutic strategies for NSCLC. This comprehensive review elucidates the significant roles and diverse mechanisms of lncRNAs in NSCLC. Furthermore, we provide insights into the clinical relevance, current research progress, limitations, innovative research approaches, and future perspectives for targeting lncRNAs in NSCLC. By summarizing the existing knowledge and advancements, we aim to enhance the understanding of the pivotal roles played by lncRNAs in NSCLC and stimulate further research in this field. Ultimately, unraveling the complex network of lncRNA-mediated regulatory mechanisms in NSCLC could potentially lead to the development of novel diagnostic tools and therapeutic strategies.

Pathogenic mycoplasmas of humans regulate the long noncoding RNAs in epithelial cells

Non-coding RNAs (ncRNAs), specifically long ncRNAs (lncRNAs), regulate cellular processes by affecting gene expression at the transcriptional, post-transcriptional, and epigenetic levels. Emerging evidence indicates that pathogenic microbes dysregulate the expression of host lncRNAs to suppress cellular defense mechanisms and promote survival. To understand whether the pathogenic human mycoplasmas dysregulate host lncRNAs, we infected HeLa cells with Mycoplasma genitalium (Mg) and Mycoplasma penumoniae (Mp) and assessed the expression of lncRNAs by directional RNA-seq analysis. HeLa cells infected with these species showed up-and-down regulation of lncRNAs expression, indicating that both species can modulate host lncRNAs. However, the number of upregulated (200 for Mg and 112 for Mp) and downregulated lncRNAs (30 for Mg and 62 for Mp) differ widely between these two species. GREAT analysis of the noncoding regions associated with differentially expressed lncRNAs showed that Mg and Mp regulate a discrete set of lncRNA plausibly related to transcription, metabolism, and inflammation. Further, signaling network analysis of the differentially regulated lncRNAs exhibited diverse pathways such as neurodegeneration, NOD-like receptor signaling, MAPK signaling, p53 signaling, and PI3K signaling, suggesting that both species primarily target signaling mechanisms. Overall, the study's results suggest that Mg and Mp modulate lncRNAs to promote their survival within the host but in distinct manners.

Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in moyamoya disease

Background: Growing evidence suggests the links between moyamoya disease (MMD) and autoimmune diseases. However, the molecular mechanism from genetic perspective remains unclear. This study aims to clarify the potential roles of autoimmune-related genes (ARGs) in the pathogenesis of MMD. Methods: Two transcription profiles (GSE157628 and GSE141025) of MMD were downloaded from GEO databases. ARGs were obtained from the Gene and Autoimmune Disease Association Database (GAAD) and DisGeNET databases. Differentially expressed ARGs (DEARGs) were identified using "limma" R packages. GO, KEGG, GSVA, and GSEA analyses were conducted to elucidate the underlying molecular function. There machine learning methods (LASSO logistic regression, random forest (RF), support vector machine-recursive feature elimination (SVM-RFE)) were used to screen out important genes. An artificial neural network was applied to construct an autoimmune-related signature predictive model of MMD. The immune characteristics, including immune cell infiltration, immune responses, and HLA gene expression in MMD, were explored using ssGSEA. The miRNA-gene regulatory network and the potential therapeutic drugs for hub genes were predicted. Results: A total of 260 DEARGs were identified in GSE157628 dataset. These genes were involved in immune-related pathways, infectious diseases, and autoimmune diseases. We identified six diagnostic genes by overlapping the three machine learning algorithms: CD38, PTPN11, NOTCH1, TLR7, KAT2B, and ISG15. A predictive neural network model was constructed based on the six genes and presented with great diagnostic ability with area under the curve (AUC) = 1 in the GSE157628 dataset and further validated by GSE141025 dataset. Immune infiltration analysis showed that the abundance of eosinophils, natural killer T (NKT) cells, Th2 cells were significant different between MMD and controls. The expression levels of HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DRB6, HLA-F, and HLA-G were significantly upregulated in MMD. Four miRNAs (mir-26a-5p, mir-1343-3p, mir-129-2-3p, and mir-124-3p) were identified because of their interaction at least with four hub DEARGs. Conclusion: Machine learning was used to develop a reliable predictive model for the diagnosis of MMD based on ARGs. The uncovered immune infiltration and gene-miRNA and gene-drugs regulatory network may provide new insight into the pathogenesis and treatment of MMD.

LINC02323 facilitates development of lung squamous cell carcinoma by miRNA sponge and RBP dysregulation and links to poor prognosis

BACKGROUND

The poor outcome of patients with lung squamous cell carcinoma (LUSC) highlights the importance of the identification of novel effective prognostic markers and therapeutic targets. Long noncoding RNAs (lncRNAs) have generally been considered to serve important roles in tumorigenesis and the development of various types of cancer, including LUSC.

METHODS

Here, we aimed to investigate the role of LINC02323 in LUSC and its potential mechanisms by performing comprehensive bioinformatic analyses.

RESULTS

LINC02323 was elevated and positively associated with unfavorable prognosis of LUSC patients. LINC02323 exerted oncogenic function by competitively binding to miR-1343-3p and miR-6783-3p, thereby upregulating L1CAM expression. Indeed, we also determined that LINC02323 could interact with the RNA-binding protein DDX3X, which regulates various stages of RNA expression and processing.

CONCLUSION

Taken together, we identified that LINC02323 and its indirect target L1CAM can act as novel biomarkers for determining the prognosis of patients with LUSC and thus deserves further study.

Necroptosis-related lncRNAs: Combination of bulk and single-cell sequencing reveals immune landscape alteration and a novel prognosis stratification approach in lung adenocarcinoma

Necroptosis, which is recently recognized as a form of programmed cell death, plays a critical role in cancer biology, including tumorigenesis and cancer immunology. It was recognized not only to defend against tumor progression by suppressing adaptive immune responses but also to promote tumorigenesis and cancer metastasis after recruiting inflammatory responses. Thus the crucial role of necrosis in tumorigenesis has attracted increasing attention. Due to the heterogeneity of the tumor immune microenvironment (TIME) in lung adenocarcinoma (LUAD), the prognosis and the response to immunotherapy vary distinctly across patients, underscoring the need for a stratification algorithm for clinical practice. Although previous studies have formulated the crucial role of lncRNAs in tumorigenicity, the relationship between necroptosis-related lncRNAs, TIME, and the prognosis of patients with LUAD was still elusive. In the current study, a robust and novel prognostic stratification model based on Necroptosis-related LncRNA Risk Scoring (NecroLRS) and clinicopathological parameters was constructed and systemically validated in both internal and external validation cohorts. The expression profile of four key lncRNAs was further validated by qRT-PCR in 4 human LUAD cell lines. And a novel immune landscape alteration was observed between NecroLRS-High and -Low patients. To further elucidate the mechanism of necroptosis in the prognosis of LUAD from a single-cell perspective, a novel stratification algorithm based on K-means clustering was introduced to extract both malignant and NecroLRS-High subsets from epithelial cells. And the necroptosis-related immune infiltration landscape and developmental trajectory were investigated respectively. Critically, NecroLRS was found to be positively correlated with neutrophil enrichment, inflammatory immune response, and malignant phenotypes of LUAD. In addition, novel ligand-receptor pairs between NecroLRS-High cells and other immunocytes were investigated and optimal therapeutic compounds were screened to provide potential targets for future studies. Taken together, our findings reveal emerging mechanisms of necroptosis-induced immune microenvironment alteration on the deteriorative prognosis and may contribute to improved prognosis and individualized precision therapy for patients with LUAD.

LncRNA RP11‑805J14.5 functions as a ceRNA to regulate CCND2 by sponging miR‑34b‑3p and miR‑139‑5p in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common lung cancer with high incidence. The prognosis of LUAD is poor due to its aggressive behavior. Long non‑coding RNAs (lncRNAs) have been reported as a key modulator on LUAD progression. Therefore, the present study aimed to clarify the molecular mechanism of lncRNAs in LUAD development. The expression of lncRNA RP11‑805J14.5 (RP11‑805J14.5) in LUAD tissues and cells was quantified based on the data in The Cancer Genome Atlas (TCGA). Cell viability was determined using Cell Counting Kit‑8 method. Apoptotic cells were sorted and determined by flow cytometry. Cell migration and invasion abilities were detected by the Transwell assay. Luciferase reporter experiment and RNA pull‑down assay were utilized to determine the interactions between RP11‑805J14.5, microRNA (miR)‑34b‑3p, miR‑139‑5p, and cyclin D2 (CCND2). A xenograft tumor was established to determine tumor growth in vivo. RP11‑805J14.5 was highly expressed in LUAD and associated with poor survival of LUAD patients. Knockdown of RP11‑805J14.5 suppressed LUAD cell growth, invasion, migration and tumor growth, indicating that RP11‑805J14.5 is an important regulator of LUAD. Our study demonstrated that the regulation of RP11‑805J14.5 on LUAD was mediated by CCND2 whose expression was regulated by sponging miR‑34b‑3p and miR‑139‑5p. The expression of RP11‑805J14.5 was increased in LUAD, and the knockdown of RP11‑805J14.5 expression suppressed LUAD cell growth, invasion and migration by downregulating CCND2 by sponging miR‑34b‑3p and miR‑139‑5p, indicating that RP11‑805J14.5 could be a prospective target for LUAD therapy.

Screening of Serum miRNAs as Diagnostic Biomarkers for Lung Cancer Using the Minimal-Redundancy-Maximal-Relevance Algorithm and Random Forest Classifier Based on a Public Database

BACKGROUND

Lung cancer is one of the deadliest cancers, early diagnosis of which can efficiently enhance patient's survival. We aimed to screening out the serum miRNAs as diagnostic biomarkers for patients with lung cancer.

METHODS

A total of 416 remarkably differentially expressed miRNAs were acquired using the limma package, and next feature ranking was derived by the minimal-redundancy-maximal-relevance method. An incremental feature selection algorithm of a random forest (RF) classifier was utilized to choose the top 5 miRNA combination with the optimum predictive performance. The performance of the RF classifier of top 5 miRNAs was analyzed using the receiver operator characteristic (ROC) curve. Afterward, the classification effect of the 5-miRNA combination was validated through principal component analysis and hierarchical clustering analysis. Analysis of top 5 miRNA expressions between lung cancer patients and normal people was performed based on GSE137140 dataset, and their expression was validated by qPCR. The hierarchical clustering analysis was used to analyze the similarity of 5 miRNAs expression profiles. ROC analysis was undertaken on each miRNA.

RESULTS

We acquired top 5 miRNAs finally, with the Matthews correlation coefficient value as 0.988 and the area under the curve (AUC) value as 0.996. The 5 feature miRNAs were capable of distinguishing most cancer patients and normal people. Furthermore, except for the lowly expressed miR-6875-5p in lung cancer tissue, the other 4 miRNAs all expressed highly in cancer patients. Performance analysis revealed that their AUC values were 0.92, 0.96, 0.94, 0.95, and 0.93, respectively.

CONCLUSION

By and large, the 5 feature miRNAs screened here were anticipated to be effective biomarkers for lung cancer.

Aerial View of the Association Between m6A-Related LncRNAs and Clinicopathological Characteristics of Pancreatic Cancer

Pancreatic cancer is a highly malignant tumor with a poor survival prognosis. We attempted to establish a robust prognostic model to elucidate the clinicopathological association between lncRNA, which may lead to poor prognosis by influencing m6A modification, and pancreatic cancer. We investigated the lncRNAs expression level and the prognostic value in 440 PDAC patients and 171 normal tissues from GTEx, TCGA, and ICGC databases. The bioinformatic analysis and statistical analysis were used to illustrate the relationship. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, univariate Cox regression and Kaplan-Meier methods were performed to identify the seven prognostic lncRNAs signatures. We inputted them in the LASSO Cox regression to establish a prognostic model in the TCGA database, verified in the ICGC database. The AUC of the ROC curve of the training set is 0.887, while the validation set is 0.711. Each patient has calculated a risk score and divided it into low-risk and high-risk subgroups by the median value. Moreover, the model showed a robust prognostic ability in the stratification analysis of different risk subgroups, pathological grades, and recurrence events. We established a ceRNA network between lncRNAs and m6A regulators. Enrichment analysis indicated that malignancy-associated biological function and signaling pathways were enriched in the high-risk subgroup and m6A-related lncRNAs target mRNA. We have even identified small molecule drugs, such as Thapsigargin, Mepacrine, and Ellipticine, that may affect pancreatic cancer progression. We found that seven lncRNAs were highly expressed in tumor patients in the GTEx-TCGA database, and LncRNA CASC19/UCA1/LINC01094/LINC02323 were confirmed in both pancreatic cell lines and FISH relative quantity. We provided a comprehensive aerial view between m6A-related lncRNAs and pancreatic cancer’s clinicopathological characteristics, and performed experiments to verify the robustness of the prognostic model.

Identification of Prognostic Factors Related to Super Enhancer-Regulated ceRNA Network in Metastatic Lung Adenocarcinoma

Introduction

The regulatory mechanisms of super enhancers (SEs) and ceRNA networks in LUAD progression are not well understood. We aimed to discover the prognostic-related ceRNA network regulated by SEs in metastatic LUAD.

Methods

RNA-seq data were extracted from The Cancer Genome Atlas (TCGA) database. Differentially expressed (DE) RNAs were identified by edgeR. CeRNA network was predicted and visualized using starBase and Cytoscape. H3K27ac ChIP-seq data were derived from the Gene Expression Omnibus (GEO) database, and used for SE identification. Kaplan–Meier curve and multivariate Cox model were applied for prognostic analysis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein–protein interaction (PPI) network were performed for functional analysis. SEs of AC074117.1 were verified by ChIP-qPCR in A549 and H1299 cells. MTT assay was performed to analyze cell proliferation. Luciferase activity assay was carried out to validate the target targeting relationships of ceRNA network.

Results

A total of 2355 DEmRNA, 483 DElncRNA and 155 DEmiRNA were identified between metastatic LUAD and adjacent normal tissues. CeRNA network consisting of 7 DElncRNAs, 18 DEmiRNAs and 15 DEmRNAs was constructed. Among the seven DElncRNAs in ceRNA network, only AC074117.1 was regulated by SEs. SE-regulated prognostic ceRNA sub-network consisting of FKBP3, E2F2, AC074117.1 and hsa-let-7c-5p was screened and verified. The overlapping co-expressed mRNAs of FKBP3, E2F2, AC074117.1 and hsa-let-7c-5p were mainly related to cell division and Fanconi anemia pathway. Genes in the ceRNA sub-network were correlated with DNA mismatch repair markers. Functional experiments proved that AC074117.1 was highly expressed in LUAD cells. AC074117.1 silencing notably inhibited proliferation of A549 and H1299 cells. Luciferase activity assay confirmed the direct relationship in AC074117.1-hsa-let-7c-5p-FKBP3/E2F2 network.

Conclusion

A novel prognostic ceRNA sub-network regulated by SEs was identified in metastatic LUAD. This study provided potential therapeutic targets and prognostic markers for further study of metastatic LUAD.

Joint changes in RNA, RNA polymerase II, and promoter activity through the cell cycle identify non-coding RNAs involved in proliferation

Proper regulation of the cell cycle is necessary for normal growth and development of all organisms. Conversely, altered cell cycle regulation often underlies proliferative diseases such as cancer. Long non-coding RNAs (lncRNAs) are recognized as important regulators of gene expression and are often found dysregulated in diseases, including cancers. However, identifying lncRNAs with cell cycle functions is challenging due to their often low and cell-type specific expression. We present a highly effective method that analyses changes in promoter activity, transcription, and RNA levels for identifying genes enriched for cell cycle functions. Specifically, by combining RNA sequencing with ChIP sequencing through the cell cycle of synchronized human keratinocytes, we identified 1009 genes with cell cycle-dependent expression and correlated changes in RNA polymerase II occupancy or promoter activity as measured by histone 3 lysine 4 trimethylation (H3K4me3). These genes were highly enriched for genes with known cell cycle functions and included 57 lncRNAs. We selected four of these lncRNAs-SNHG26, EMSLR, ZFAS1, and EPB41L4A-AS1-for further experimental validation and found that knockdown of each of the four lncRNAs affected cell cycle phase distributions and reduced proliferation in multiple cell lines. These results show that many genes with cell cycle functions have concomitant cell-cycle dependent changes in promoter activity, transcription, and RNA levels and support that our multi-omics method is well suited for identifying lncRNAs involved in the cell cycle.

Identifying the Signatures and Rules of Circulating Extracellular MicroRNA for Distinguishing Cancer Subtypes

Cancer is one of the most threatening diseases to humans. It can invade multiple significant organs, including lung, liver, stomach, pancreas, and even brain. The identification of cancer biomarkers is one of the most significant components of cancer studies as the foundation of clinical cancer diagnosis and related drug development. During the large-scale screening for cancer prevention and early diagnosis, obtaining cancer-related tissues is impossible. Thus, the identification of cancer-associated circulating biomarkers from liquid biopsy targeting has been proposed and has become the most important direction for research on clinical cancer diagnosis. Here, we analyzed pan-cancer extracellular microRNA profiles by using multiple machine-learning models. The extracellular microRNA profiles on 11 cancer types and non-cancer were first analyzed by Boruta to extract important microRNAs. Selected microRNAs were then evaluated by the Max-Relevance and Min-Redundancy feature selection method, resulting in a feature list, which were fed into the incremental feature selection method to identify candidate circulating extracellular microRNA for cancer recognition and classification. A series of quantitative classification rules was also established for such cancer classification, thereby providing a solid research foundation for further biomarker exploration and functional analyses of tumorigenesis at the level of circulating extracellular microRNA.

Novel long noncoding RNA LINC02323 promotes cell growth and migration of ovarian cancer via TGF-β receptor 1 by miR-1343-3p

BACKGROUND

This study was aimed at investigating the effects of long noncoding RNA (lncRNA) LINC02323 in ovarian cancer and its possible mechanism.

METHODS

Microarray analysis and QPCR were utilized to identify lncRNA LINC02323 expression in patients with ovarian cancer. MTT assay was used for analysis of ovarian cancer cell proliferation. Western blot was utilized to investigate its possible mechanism.

RESULTS

In patients with ovarian cancer, lncRNA LINC02323 expression was up-regulated and miR-1343-3p expression was down-regulated. Over-expression of lncRNA LINC02323 promoted cell growth and reduced LDH activity levels in vitro model by suppression of miR-1343-3p expression. Down-regulation of lncRNA LINC02323 reduced cell growth and increased LDH activity levels in vitro model by induction of miR-1343-3p expression. Over-expression of miR-1343-3p reduced cell growth and reduced LDH activity levels in vitro model by suppression of TGF-β receptor. Down-regulation of miR-1343-3p promoted cell growth and reduced LDH activity levels in vitro model by induced of TGF-β receptor.

CONCLUSION

Our findings show that Novel long noncoding RNA LINC02323 promotes cell growth of ovarian cancer via TGF-β receptor 1 by miR-1343-3p.