Long noncoding RNA LINC01426 promotes the progression of lung adenocarcinoma via regulating miRNA-125a-5p/ casein kinase 2 alpha 1 axis

Differential expression and clinical significance of long non-coding RNAs in the development and progression of lung adenocarcinoma

With the development of gene testing technology, we have found many different genes, and lncRNA is one of them. LncRNAs refer to a non-protein coding RNA molecule with a length of more than 200bp, which is one of the focuses of research on human malignant diseases such as LUAD. LncRNAs act as an oncogene or inhibitor to regulate the occurrence and progression of tumors. The differential expression of LncRNAs promotes or inhibits the progression of lung adenocarcinoma by affecting cell proliferation, metastasis, invasion, and apoptosis, thus affecting the prognosis and survival rate of patients. Therefore, LncRNAs can be used as a potential target for diagnosis and treatment of cancer. The early diagnosis of the disease was made through the detection of tumor markers. Because lung adenocarcinoma is not easy to diagnose in the early stage and tumor markers are easy to ignore, LncRNAs play an important role in the diagnosis and treatment of lung adenocarcinoma. The main purpose of this article is to summarize the known effects of LncRNAs on lung adenocarcinoma, the effect of differential expression of LncRNAs on the progression of lung adenocarcinoma, and related signal transduction pathways. And to provide a new idea for the future research of lung adenocarcinoma-related LncRNAs.

Diagnostic value and cognitive regulatory roles of long non-coding RNA UCA1 in Alzheimer's disease

BACKGROUND

To explore the diagnostic role and potential mechanism of serum lncRNA UCA1 in Alzheimer's disease (AD).

METHODS

UCA1 concentration was determined using quantitative RT-PCR. The receiver operating characteristic curve was plotted to assess the diagnostic value. Cell viability and apoptotic capacity were assessed by cell counting kit-8 (CCK-8) and flow cytometry. Water maze experiments were used to test cognitive function in mice. The target genes of UCA1 were identified with a dual luciferase reporter assay. Functional and pathway analysis of miR-342-3p target genes was determined using enrichment analysis.

RESULTS

The concentration of UCA1 was elevated in the AD group and represented a diagnostic possibility of AD. The silenced UCA1 reduced the roles of Aβ on viability and apoptosis of SH⁃SY5Y cells by sponging miR-342-3p. The impaired cognitive impairment was partly recovered by the knockdown of the UCA1/miR-342-3p axis. Potential targets of miR-342-3p were enriched in function and pathways related to AD progression.

CONCLUSION

The UCA1/miR-342-3p axis contributed to the occurrence of AD by regulating cognitive ability.

Unravelling the role of long non-coding RNAs in modulating the Hedgehog pathway in cancer

Cancer is a multifactorial pathological condition characterized by uncontrolled cellular proliferation, genomic instability, and evasion of regulatory mechanisms. It arises from the accumulation of genetic mutations confer selective growth advantages, leading to malignant transformation and tumor formation. The intricate interplay between LncRNAs and the Hedgehog pathway has emerged as a captivating frontier in cancer research. The Hedgehog pathway, known for its fundamental roles in embryonic development and tissue homeostasis, is frequently dysregulated in various cancers, contributing to aberrant cellular proliferation, survival, and differentiation. The Hh pathway is crucial in organizing growth and maturation processes in multicellular organisms. It plays a pivotal role in the initiation of tumors as well as in conferring resistance to conventional therapeutic approaches. The crosstalk among the Hh pathway and lncRNAs affects the expression of Hh signaling components through various transcriptional and post-transcriptional processes. Numerous pathogenic processes, including both non-malignant and malignant illnesses, have been identified to be induced by this interaction. The dysregulation of lncRNAs has been associated with the activation or inhibition of the Hh pathway, making it a potential therapeutic target against tumorigenesis. Insights into the functional significance of LncRNAs in Hedgehog pathway modulation provide promising avenues for diagnostic and therapeutic interventions. The dysregulation of LncRNAs in various cancer types underscores their potential as biomarkers for early detection and prognostication. Additionally, targeting LncRNAs associated with the Hedgehog pathway presents an innovative strategy for developing precision therapeutics to restore pathway homeostasis and impede cancer progression. This review aims to elucidate the complex regulatory network orchestrated by LncRNAs, unravelling their pivotal roles in modulating the Hedgehog pathway and influencing cancer progression.

RPIPCM: A deep network model for predicting lncRNA-protein interaction based on sequence feature encoding

LncRNA-protein interactionplays an important regulatory role in biological processes. In this paper, the proposed RPIPCM based on a novel deep network model uses the sequence feature encoding of both RNA and protein to predict lncRNA-protein interactions (LPIs). A negative sampling of sliding window method is proposed for solving the problem of unbalanced between positive and negative samples. The proposed negative sampling method is effective and helpful to solve the problem of data imbalance in the existing LPIs research by comparative experiments. Experimental results also show that the proposed sequence feature encoding method has good performance in predicting LPIs for different datasets of different sizes and types. In the RPI488 dataset related to animal, compared with the direct original sequence encoding model, the accuracy of sequence feature encoding model increased by 1.02%, the recall increased by 4.08%, and the value of MCC increased by 1.67%. In the case of the plant dataset ATH948, the sequence feature-based encoding demonstrated a 1.58% higher accuracy, a 1.53% higher recall, a 1.62% higher specificity, a 1.62% higher precision, and a 3.16% higher value of MCC compared to the direct original sequence-based encoding. Compared with the latest prediction work in the ZEA22133 dataset, RPIPCM is shown to be more effective with the accuracy increased by 2.23%, the recall increased by 1.78%, the specificity increased by 2.67%, the precision increased by 2.52%, and the value of MCC increased by 4.43%, which also proves the effectiveness and robustness of RPIPCM. In conclusion, RPIPCM of deep network model based on sequence feature encoding can automatically mine the hidden feature information of the sequence in the lncRNA-protein interaction without relying on external features or prior biomedical knowledge, and its low cost and high efficiency can provide a reference for biomedical researchers.

Identification and validation of ferroptosis-related lncRNA signatures as a novel prognostic model for glioma

Background: Ferroptosis is a newly discovered form of regulated cell death with distinct properties and recognizing functions involved in physical conditions or various diseases, including cancers. However, the relationship between gliomas and ferroptosis-related lncRNAs (FRLs) remains unclear.

Methods: We collected a total of 1850 samples from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEX) databases, including 698 tumor and 1,152 normal samples. A list of ferroptosis-related genes was downloaded from the Ferrdb website. Differentially expressed FRLs (DEFRLS) were analyzed using the “limma” package in R software. Subsequently, prognosis-related FRLs were obtained by univariate Cox analysis. Finally, a prognostic model based on the 3 FRLs was constructed using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm. The prognostic power of the model was assessed using receiver operating characteristic (ROC) curve analysis and Kaplan-Meier (K-M) survival curve analysis. In addition, we further explored the relationship of the immune landscape and somatic mutations to prognostic model characteristics. Finally, we validated the function of LINC01426 in vitro.

Results: We successfully constructed a 3-FRLs signature and classified glioma patients into high-risk and low-risk groups based on the risk score calculated from this signature. Compared with traditional clinicopathological features [age, sex, grade, isocitrate dehydrogenase (IDH) status], the prognostic accuracy of this model is more stable and stronger. Additionally, the model had stable predictive power for overall survival over a 5-year period. In addition, we found significant differences between the two groups in cellular immunity, the numbers of many immune cells, including NK cells, CD4+, CD8+ T-cells, and macrophages, and the expression of many immune-related genes. Finally, the two groups were also significantly different at the level of somatic mutations, especially in glioma prognosis-related genes such as IDH1 and ATRX, with lower mutation rates in the high-risk group leading to poorer prognosis. Finally, we found that the ferroptosis process of glioma cells was inhibited after knocking down the expression of LINC01426.

Conclusion: The proposed 3-FRL signature is a promising biomarker for predicting prognostic features in glioma patients.

LINC01426 Triggers Growth and Metastasis of Lung Adenocarcinoma as a Prognostic Indicator

The vital regulation of abnormally expressed lncRNAs in human cancers has been identified. This study is aimed at illustrating the role of LINC01426 in influencing malignant behaviors of lung adenocarcinoma (LUAD) and the possible mechanism. Differential expressions of LINC01426 in a downloaded profile containing LUAD and normal tissues were analyzed using Gene Expression Profiling Interactive Analysis (GEPIA) database and were reconfirmed in clinical samples collected in our hospital. In addition, LINC01426 level in lung carcinoma cell lines was detected by quantitative real-time polymerase chain reaction (qRT-PCR) as well. The relationship between LINC01426 expression and the age, tumor node metastasis (TNM) staging, lymphatic metastasis, tumor differentiation, and overall survival of LUAD was analyzed. After intervening LINC01426 level in H1299 and PC9 cells, proliferative and metastatic changes were assessed by functional experiments. LINC01426 was upregulated in LUAD tissues and cell lines. Its level was closely linked to TNM staging, lymphatic metastasis, tumor differentiation, and overall survival of LUAD. Knockdown of LINC01426 suppressed proliferative and metastatic abilities in H1299 and PC9 cells. LINC01246 is upregulated in LUAD samples and predicts a poor prognosis. It drives malignant process of LUAD via stimulating proliferative and metastatic abilities.