Pan-cancer analysis of LncRNA XIST and its potential mechanisms in human cancers

Long noncoding RNA TMPO-AS1 upregulates BCAT1 expression to promote cell proliferation in nasopharyngeal carcinoma via microRNA let-7c-5p

BACKGROUND

Long non-coding RNA (lncRNA) is a group of RNA transcripts that contribute to tumor development by post-transcriptionally regulating cancer-related genes. Nasopharyngeal carcinoma (NPC) is an epithelial tumor that occurs in the nasopharynx and is common in North Africa and Southeast Asia. The study investigated the functions of lncRNA TMPO-AS1 in NPC cell proliferation and apoptosis as well as its related competing endogenous RNA (ceRNA) mechanism.

METHODS

Candidate microRNA and genes that may regulated by TMPO-AS1 were predicted with the bioinformatic tool starBase. TMPO-AS1 expression in NPC tissue, cells, nuclear part, and cytoplasmic part was measured by RT-qPCR. MTT assay, EdU assay, and flow cytometry analysis were carried out to evaluate NPC cell viability, proliferation, and apoptosis, respectively. RNA immunoprecipitation assay and luciferase reporter assay were conducted to detect the binding between TMPO-AS1 and let-7c-5p or that between let-7c-5p and BCAT1.

RESULTS

TMPO-AS1 and BCAT1 showed high expression in NPC tissue and cells, while let-7c-5p was downregulated in NPC. The silencing of TMPO-AS1 suppressed NPC cell proliferation while promoting cell apoptosis. Moreover, TMPO-AS1 interacted with let-7c-5p and negatively regulated let-7c-5p expression. BCAT1 was a target of let-7c-5p and was inversely regulated by let-7c-5p in NPC cells. The repressive impact of TMPO-AS1 knockdown on NPC cell growth was countervailed by overexpressed BCAT1.

CONCLUSION

TMPO-AS1 accelerates NPC cell proliferation and represses cell apoptosis by interacting with let-7c-5p to regulate BCAT1 expression.

Hallmarks of sex bias in immuno-oncology: mechanisms and therapeutic implications

Sex differences are present across multiple non-reproductive organ cancers, with male individuals generally experiencing higher incidence of cancer with poorer outcomes. Although some mechanisms underlying these differences are emerging, the immunological basis is not well understood. Observations from clinical trials also suggest a sex bias in conventional immunotherapies with male individuals experiencing a more favourable response and female individuals experiencing more severe adverse events to immune checkpoint blockade. In this Perspective article, we summarize the major biological hallmarks underlying sex bias in immuno-oncology. We focus on signalling from sex hormones and chromosome-encoded gene products, along with sex hormone-independent and chromosome-independent epigenetic mechanisms in tumour and immune cells such as myeloid cells and T cells. Finally, we highlight opportunities for future studies on sex differences that integrate sex hormones and chromosomes and other emerging cancer hallmarks such as ageing and the microbiome to provide a more comprehensive view of how sex differences underlie the response in cancer that can be leveraged for more effective immuno-oncology approaches.

Discovering therapeutic possibilities for polycystic ovary syndrome by targeting XIST and its associated ceRNA network through the analysis of transcriptome data

Long non-coding RNA (lncRNA) regulates many physiological processes by acting as competitive endogenous RNA (ceRNA). The dysregulation of lncRNA X-inactive specific transcript (XIST) has been shown in various human disorders. However, its role in the pathogenesis of polycystic ovary syndrome (PCOS) is yet to be explored. This study aimed to explore the underlying mechanism of XIST in the pathogenesis of PCOS, specifically through dataset functional analysis. GEO PCOS datasets including RNA-seq, microarray, and miRNA-seq in granulosa cells (GCs) and blood, were examined and comprehensively analyzed. Enrichment analysis, ROC curve constructions, lncRNA-miRNA-mRNA interaction network analyses, and qRT-PCR validation were performed followed by a series of drug signature screenings. Our results revealed significant dysregulation in the expression of 1131 mRNAs, 30 miRNAs, and XIST in GCs of PCOS patients compared to healthy individuals. Of the120 XIST-correlated upregulated genes, 25 were enriched in inflammation-related pathways. Additionally, 5 miRNAs were identified as negative regulators of XIST-correlated genes. Accordingly, a ceRNA network containing XIST-miRNAs-mRNAs interactions was constructed. Furthermore, 6 genes, including AQP9, ETS2, PLAU, PLEK, SOCS3, and TNFRSF1B served as both GCs and blood-based biomarkers. By analyzing the number of interactions among XIST, miRNAs, and mRNAs, we pinpointed ETS2 as the pivotal gene within the ceRNA network. Our findings reveal a novel XIST- hsa-miR-146a-5p, hsa-miR-144-3p, and hsa-miR-1271-5p-ETS2 axis that comprehensively elucidates the XIST-associated mechanism underlying PCOS onset. qRT-PCR analysis further confirmed the, overexpression of both XIST and ETS2 . Furthermore, our results demonstrated that XIST and ETS2 were correlated with some assisted reproductive technologies outcomes. Finally, we identified two novel compounds including, methotrexate/folate and threonine using drug-gene interaction databases for PCOS management. These findings provide novel insights into the molecular etiology, diagnosis, and potential therapeutic interventions for PCOS.

Pan-cancer analyses identify MIR210HG overexpression, epigenetic regulation and oncogenic role in human tumors and its interaction with the tumor microenvironment

BACKGROUND

Molecular entities showing dysregulation in multiple cancers may hold great biomarker or therapeutic potential. There is accumulating evidence that highlights the dysregulation of a long non-coding RNA, MIR210HG, in various cancers and its oncogenic role. However, a comprehensive analysis of MIR210HG expression pattern, molecular mechanisms, diagnostic or prognostic significance or evaluation of its interaction with tumor microenvironment across various cancers remains unstudied.

METHODS

A systematic pan-cancer analysis was done using multiple public databases and bioinformatic tools to study the molecular role and clinical significance of MIR210HG. We have analyzed expression patterns, genome alteration, transcriptional and epigenetic regulation, correlation with patient survival, immune infiltrates, co-expressed genes, interacting proteins, and pathways associated with MIR210HG.

RESULTS

The Pan cancer expression analysis of MIR210HG through various tumor datasets demonstrated that MIR210HG is significantly upregulated in most cancers and increased with the tumor stage in a subset of them. Furthermore, prognostic analysis revealed high MIR210HG expression is associated with poor overall and disease-free survival in specific cancer types. Genetic alteration analysis showed minimal alterations in the MIR210HG locus, indicating that overexpression in cancers is not due to gene amplification. The exploration of SNPs on MIR210HG suggested possible structural changes that may affect its interactions with the miRNAs. The correlation of MIR210HG with promoter methylation was found to be significantly negative in nature in majority of cancers depicting the possible epigenetic regulation of expression of MIR210HG. Additionally, MIR210HG showed negative correlations with immune cells and thus may have strong impact on the tumor microenvironment. Functional analysis indicates its association with hypoxia, angiogenesis, metastasis, and DNA damage repair processes. MIR210HG was found to interact with several proteins and potentially regulate chromatin modifications and transcriptional regulation.

CONCLUSIONS

A first pan-can cancer analysis of MIR210HG highlights its transcriptional and epigenetic deregulation and oncogenic role in the majority of cancers, its correlation with tumor microenvironment factors such as hypoxia and immune infiltration, and its potential as a prognostic biomarker and therapeutic target in several cancers.

Non-coding RNA profile for natural killer cell activity

Natural killer cells (NK cells) are a type of cytotoxic lymphocytes which are involved in innate immunity, alongside with assisting with adaptive immune response. Since they have cytotoxic effects, disruptions in their functionality and development leads to a variety of conditions, whether malignant or non-malignant. The profile and interaction of these non-coding RNAs and NK cells in different conditions is extensively studied, and it is now approved that if dysregulated, non-coding RNAs have detrimental effects on NK cell activity and can contribute to the pathogenesis of diverse disorders. In this review, we aim at a thorough inspection on the role of different non-coding RNAs on the activity and development of NK cells, in a broad spectrum of conditions, including blood-related disorders, viral infections, neurological diseases, gastrointestinal disorders, lung disorders, reproductive system conditions and other types of maladies, alongside with providing insight to the future non-coding RNA-NK cell studies.

A lncRNA-disease association prediction model based on the two-step PU learning and fully connected neural networks

Long non-coding RNAs (lncRNAs) have been shown to play a regulatory role in various processes of human diseases. However, lncRNA experiments are inefficient, time-consuming and highly subjective, so that the number of experimentally verified associations between lncRNA and diseases is limited. In the era of big data, numerous machine learning methods have been proposed to predict the potential association between lncRNA and diseases, but the characteristics of the associated data were seldom explored. In these methods, negative samples are randomly selected for model training and the model is prone to learn the potential positive association error, thus affecting the prediction accuracy. In this paper, we proposed a cyclic optimization model of predicting lncRNA-disease associations (COPTLDA in short). In COPTLDA, the two-step training strategy is adopted to search for the samples with the greater probability of being negative examples from unlabeled samples and the determined samples are treated as negative samples, which are combined together with known positive samples to train the model. The searching and training steps are repeated until the best model is obtained as the final prediction model. In order to evaluate the performance of the model, 30% of the known positive samples are used to calculate the model accuracy and 10% of positive samples are used to calculate the recall rate of the model. The sampling strategy used in this paper can improve the accuracy and the AUC value reaches 0.9348. The results of case studies showed that the model could predict the potential associations between lncRNA and malignant tumors such as colorectal cancer, gastric cancer, and breast cancer. The predicted top 20 associated lncRNAs included 10 colorectal cancer lncRNAs, 2 gastric cancer lncRNAs, and 8 breast cancer lncRNAs.