Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview

Biological roles of SLC16A1-AS1 lncRNA and its clinical impacts in tumors

Recent studies have increasingly highlighted the aberrant expression of SLC16A1-AS1 in a variety of tumor types, where it functions as either an oncogene or a tumor suppressor in the pathogenesis of different cancers. The expression levels of SLC16A1-AS1 have been found to significantly correlate with clinical features and the prognosis of cancer patients. Furthermore, SLC16A1-AS1 modulates a range of cellular functions, including proliferation, migration, and invasion, through its interactions with diverse molecules and signaling pathways. This review examines the latest evidence regarding the role of SLC16A1-AS1 in the progression of various tumors and explores its potential clinical applications as a novel prognostic and diagnostic biomarker. Our comprehensive review aims to deepen the understanding of SLC16A1-AS1's multifaceted role in oncology, underscoring its potential as a significant biomarker and therapeutic target.

The Emerging Role of Non-Coding RNAs (ncRNAs) in Plant Growth, Development, and Stress Response Signaling

Plant species utilize a variety of regulatory mechanisms to ensure sustainable productivity. Within this intricate framework, numerous non-coding RNAs (ncRNAs) play a crucial regulatory role in plant biology, surpassing the essential functions of RNA molecules as messengers, ribosomal, and transfer RNAs. ncRNAs represent an emerging class of regulators, operating directly in the form of small interfering RNAs (siRNAs), microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs). These ncRNAs exert control at various levels, including transcription, post-transcription, translation, and epigenetic. Furthermore, they interact with each other, contributing to a variety of biological processes and mechanisms associated with stress resilience. This review primarily concentrates on the recent advancements in plant ncRNAs, delineating their functions in growth and development across various organs such as root, leaf, seed/endosperm, and seed nutrient development. Additionally, this review broadens its scope by examining the role of ncRNAs in response to environmental stresses such as drought, salt, flood, heat, and cold in plants. This compilation offers updated information and insights to guide the characterization of the potential functions of ncRNAs in plant growth, development, and stress resilience in future research.

The pivotal role of long non-coding RNAs as potential biomarkers and modulators of chemoresistance in ovarian cancer (OC)

Ovarian cancer (OC) is a fatal gynecological disease that is often diagnosed at later stages due to its asymptomatic nature and the absence of efficient early-stage biomarkers. Previous studies have identified genes with abnormal expression in OC that couldn't be explained by methylation or mutation, indicating alternative mechanisms of gene regulation. Recent advances in human transcriptome studies have led to research on non-coding RNAs (ncRNAs) as regulators of cancer gene expression. Long non-coding RNAs (lncRNAs), a class of ncRNAs with a length greater than 200 nucleotides, have been identified as crucial regulators of physiological processes and human diseases, including cancer. Dysregulated lncRNA expression has also been found to play a crucial role in ovarian carcinogenesis, indicating their potential as novel and non-invasive biomarkers for improving OC management. However, despite the discovery of several thousand lncRNAs, only one has been approved for clinical use as a biomarker in cancer, highlighting the importance of further research in this field. In addition to their potential as biomarkers, lncRNAs have been implicated in modulating chemoresistance, a major problem in OC. Several studies have identified altered lncRNA expression upon drug treatment, further emphasizing their potential to modulate chemoresistance. In this review, we highlight the characteristics of lncRNAs, their function, and their potential to serve as tumor markers in OC. We also discuss a few databases providing detailed information on lncRNAs in various cancer types. Despite the promising potential of lncRNAs, further research is necessary to fully understand their role in cancer and develop effective strategies to combat this devastating disease.

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

RNA-Sequencing Characterization of lncRNA and mRNA Functions in Septic Pig Liver Injury

We assessed differentially expressed (DE) mRNAs and lncRNAs in the liver of septic pigs to explore the key factors regulating lipopolysaccharide (LPS)-induced liver injury. We identified 543 DE lncRNAs and 3642 DE mRNAs responsive to LPS. Functional enrichment analysis revealed the DE mRNAs were involved in liver metabolism and other pathways related to inflammation and apoptosis. We also found significantly upregulated endoplasmic reticulum stress (ERS)-associated genes, including the receptor protein kinase receptor-like endoplasmic reticulum kinase (PERK), the eukaryotic translation initiation factor 2α (EIF2S1), the transcription factor C/EBP homologous protein (CHOP), and activating transcription factor 4 (ATF4). In addition, we predicted 247 differentially expressed target genes (DETG) of DE lncRNAs. The analysis of protein-protein interactions (PPI) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway detected key DETGs that are involved in metabolic pathways, such as N-Acetylgalactosaminyltransferase 2 (GALNT2), argininosuccinate synthetase 1 (ASS1), and fructose 1,6-bisphosphatase 1 (FBP1). LNC_003307 was the most abundant DE lncRNA in the pig liver, with a marked upregulation of >10-fold after LPS stimulation. We identified three transcripts for this gene using the rapid amplification of the cDNA ends (RACE) technique and obtained the shortest transcript sequence. This gene likely derives from the nicotinamide N-methyltransferase (NNMT) gene in pigs. According to the identified DETGs of LNC_003307, we hypothesize that this gene regulates inflammation and endoplasmic reticulum stress in LPS-induced liver damage in pigs. This study provides a transcriptomic reference for further understanding of the regulatory mechanisms underlying septic hepatic injury.

Identification of hub biomarkers and exploring the roles of immunity, M6A, ferroptosis, or cuproptosis in rats with diabetic erectile dysfunction

BACKGROUND

Currently, patients with diabetic erectile dysfunction (DMED) were not satisfied with the effects of first-line phosphodiesterase type 5 inhibitors (PDE5Is). Hence, this paper was designed to mine hub biomarkers in DMED and explore its potential mechanisms.

METHODS

Gene expression matrix of DMED was downloaded from the gene expression omnibus (GEO; GSE2457) dataset. The top 20 genes were selected based on the connectivity degrees in protein-protein interaction (PPI) network. Functional enrichment analysis was utilized to reveal DMED-related signaling pathways. We also explored the roles of immunity, m6A, ferroptosis, or cuproptosis in DMED and constructed Sprague Dawley (SD) rats DMED model to verify gene expressions by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

Based on the threshold, a total of 122 differently expressed genes (DEGs) were identified in DMED, including 39 up-regulated and 83 down-regulated genes. Functional enrichment analysis implied that these DEGs were significantly enriched in peroxisome proliferator-activated receptors, ferroptosis, hypoxia-inducible factor 1 signaling pathways, and so on. SD rats DMED model was also successfully established by us and validated by intracavernous pressure/mean arterial pressure, Masson's trichrome staining, and immunohistochemical analysis. We further verified the expression of these top 20 genes from the PPI network by qRT-PCR in the SD rats DMED model and finally identified Sparc, Lox, Srebf1, and Mmp3 as hub biomarkers (all p < 0.05). As for immunity and cuproptosis, our analysis indicated that DMED had nothing to do with them (all p > 0.05). Actually, DMED was markedly associated with m6A regulators and ferroptosis.

CONCLUSIONS

We identified Sparc, Lox, Srebf1, and Mmp3 as potential hub biomarkers in the SD rats DMED model for future drug development and found its significant associations with m6A regulators and ferroptosis, but not with immunity or cuproptosis.

LINC01296 promotes proliferation of cutaneous malignant melanoma by regulating miR-324-3p/MAPK1 axis

OBJECTIVE

To investigate the functions and potential molecular mechanism of LINC01296 regarding the progression of cutaneous malignant melanoma (CMM) by the regulation of miR-324-3p/MAPK1 axis.

METHODS

The candidate differential lncRNAs of CMM were selected from GEPIA database, and quantitative real-time PCR (qRT-PCR) was utilized to assess the expression level of LINC01296 in human CMM tissues and cell lines. Cell proliferation assay, Colony formation assay, Ethynyl-2'-deoxyuridine (EDU) assay in vitro and tumorigenicity assays in nude mice in vivo were performed to examine the functions of LINC01296. Bioinformatics analysis, luciferase reporter assay and rescue experiments were also gained an insight into the underlying mechanisms of LINC01296 in CMM cell lines by miR-324-3p/MAPK1 axis.

RESULTS

In this study, the up-regulation of LINC01296 was found in CMM tissues and cell lines. Functionally, the over-expression of LINC01296 promoted the proliferation in CMM cell lines. In addition, immunochemistry analysis confirmed that the levels of MAPK1 and Ki-67 in sh-LINC01296-xenografted tumors was weaker than that in sh-NC-xenografted tumors. Then, bioinformatics analysis confirmed that LINC01296 interacted with miR-324-3p. Further investigations showed that MAPK1, which collected from the potential related genes of LINC01296, was the conjugated mRNA of miR-324-3p by luciferase reporter assay. Finally, the rescue experiments suggested the positive regulatory association among LINC01296 and MAPK1, which showed that MAPK1 could reverse the promoting-effect of LINC01296 in CMM cells in vitro.

CONCLUSIONS

Therefore, our findings provided insight into the mechanisms of LINC01296 via miR-324-3p/MAPK1 axis in CMM, and revealed an alternative target for the diagnosis and treatment of CMM.

Identification of Gefitinib Resistance-Related lncRNA-miRNA-mRNA Regulatory Networks and Corresponding Prognostic Signature in Patients with Lung Adenocarcinoma

Purpose

To identify and characterize gefitinib resistance-related (GefR-related) lncRNAs and construct a prediction model for lung adenocarcinoma (LUAD).

Methods

Differential expression analysis between PC9 and gefitinib-resistant PC9 (PC9GR) cell samples was performed to screen GefR-related lncRNAs and mRNAs based on the GSE34228 dataset. These lncRNAs, mRNAs, and their corresponding microRNAs (miRNAs) were used to construct the GefR-related network and PPI networks. Functional enrichment analyses were conducted using the STRING database. A prognostic signature was developed using the TCGA dataset. The reliability of the signature was tested using the Kaplan–Meier method and ROC curve. Lastly, the FZD4-associated ceRNA subnetwork was selected to confirm the in vitro expressions of the GefR-related lncRNAs using RT-qPCR assay.

Results

A GefR-related ceRNA network that consists of 35 miRNAs, 26 lncRNAs, and 179 mRNAs was constructed. Then, 20 hub genes were screened from the targeted mRNAs of the constructed PPI network, and enrichment analysis identified relevant enriched pathways. We also constructed a prognostic signature for LUAD based on nine mRNAs in the GefR-related ceRNA network. The 9-mRNA signature was an independent predictor of LUAD, the AUC produced by ROC analysis showed a good predictive power of the model, and Kaplan–Meier analysis showed poorer outcomes in the high-risk group, relative to the low-risk group. Lastly, MIR137HG and ZNF295-AS1 levels were found to be associated with gefitinib resistance and exerted their functions through the ceRNA mechanism.

Conclusion

We established a prognostic signature and identified two lncRNAs (MIR137HG and ZNF295-AS1) with potential significant roles in gefitinib resistance.

The regulatory effect and molecular mechanism of lncRNA Gm10451 on islet cell dysfunction in children with diabetes

The dysfunction of islet β-cells is one of the causes of diabetes, and lncRNA Gm10451 is also a participant in the occurrence and the development of various diseases. This study was carried out to reveal the correlation within β-cells and Gm10451. Our study was started with the cellular cultivation of MIN6 cells in vitro, where this islet β-cell line was randomly divided into the groups of control, hyperglycemia, Gm10451 siRNA tansfection, and Gm10451 tansfection. Of all these treatments, cells in the groups of Gm10451 siRNA tansfection and Gm10451 tansfection were given with lentiviral transfection under hyperglycemia condition. Further explorations were established using PCR assay and MTT method to evaluate Gm10451 expression and estimate cellular proliferation. It ended up with the enzyme-linked immunosorbent assay (ELISA) to assess Caspase 3 activity, superoxide dismutase (SOD) activity, and reactive oxygen species (ROS) content and the secretion of IL-10 and IL-1. It was found that Gm10451 expression in MIN6 cells under hyperglycemia cultivation was notably higher than the control group; likewise, a transfection with the lentivirus of Gm10451 also resulted in the upregulation of Gm10451 expression, succeeded with inhibiting cellular proliferation, enhancing Caspase 3 activity, and decreasing SOD activity. In the lentivirus transfection groups, transfection of Gm10451 elevated the ROS content and promoted IL-1 expression, and it also decreased both IL-10 expression and insulin secretion, leading to a consequence of statistically significant difference in contrast to the high-glucose group; on the contrary, transfection of Gm10451 siRNA in a high-glucose environment downregulated the expression of Gm10451 and inversed those change before, whose results were statistically significant when compared with the high-glucose group. Hyperglycemia promotes the expression of Gm10451. Targeting inhibition toward Gm10451 alleviates cellular apoptosis and the oxidative stress of islet cells, promoting proliferation and insulin secretion of islet cells.

PIWI-interacting RNAs in human diseases: databases and computational models

PIWI-interacting RNAs (piRNAs) are short 21-35 nucleotide molecules that comprise the largest class of non-coding RNAs and found in a large diversity of species including yeast, worms, flies, plants and mammals including humans. The most well-understood function of piRNAs is to monitor and protect the genome from transposons particularly in germline cells. Recent data suggest that piRNAs may have additional functions in somatic cells although they are expressed there in far lower abundance. Compared with microRNAs (miRNAs), piRNAs have more limited bioinformatics resources available. This review collates 39 piRNA specific and non-specific databases and bioinformatics resources, describes and compares their utility and attributes and provides an overview of their place in the field. In addition, we review 33 computational models based upon function: piRNA prediction, transposon element and mRNA-related piRNA prediction, cluster prediction, signature detection, target prediction and disease association. Based on the collection of databases and computational models, we identify trends and potential gaps in tool development. We further analyze the breadth and depth of piRNA data available in public sources, their contribution to specific human diseases, particularly in cancer and neurodegenerative conditions, and highlight a few specific piRNAs that appear to be associated with these diseases. This briefing presents the most recent and comprehensive mapping of piRNA bioinformatics resources including databases, models and tools for disease associations to date. Such a mapping should facilitate and stimulate further research on piRNAs.

Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma

Background

The current research aimed to develop an artificial intelligence predictive system for individual survival rate of lung adenocarcinoma (LUAD).

Methods

Independent risk variables were identified by multivariate Cox regression. Artificial intelligence predictive system was constructed using three different data mining algorithms.

Results

Stage, PM, chemotherapy, PN, age, PT, sex, and radiation_surgery were determined as risk factors for LUAD patients. For 12-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.852, 0.821, and 0.835, respectively. For 36-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.901, 0.864, and 0.862, respectively. For 60-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.899, 0.874, and 0.866, respectively. The concordance indexes in validation dataset were similar to those in model dataset.

Conclusions

The current study designed an individualized survival predictive system, which could provide individual survival curves using three different artificial intelligence algorithms. This artificial intelligence predictive system could directly convey treatment benefits by comparing individual mortality risk curves under different treatments. This artificial intelligence predictive tool is available at https://zhangzhiqiao11.shinyapps.io/Artificial_Intelligence_Survival_Prediction_System_AI_E1001/.