Mitochondria-related core genes and TF-miRNA-hub mrDEGs network in breast cancer

Deciphering the miRNA-mRNA Interaction Landscape between Breast Cancer and Triple-Negative Breast Cancer: An Integrated Bioinformatics Approach

Breast cancer (BC) is globally recognized as the second most prevalent form of cancer. It predominantly affects women and can be categorized into distinct types based on the overexpression of specific cancer receptors.The key receptors implicated in this context are the human epidermal growth factor receptor-2 (HER2), estrogen receptor (ER), and progesterone receptor (PR), alongside a particularly intricate subclass known as triple-negative breast cancer (TNBC). This subclassification is critical for the stratification of breast cancer and informs therapeutic decision-making processes. Due to a lack of therapeutic targets, such as growth factor receptors, TNBC is the most aggressive type. Hence, identifying targetable regulators such as miRNAs could pave the way for potential therapeutic interventions. To identify common differentially expressed mRNAs (DE-mRNAs) in BC, including TNBC, we leveraged two data sets from the GEO collection and The Cancer Genome Atlas (TCGA). Significant DE-mRNAs were identified through PPI, MCODE, CytoNCA, and CytoHubba analyses. Following this, miRNAs were predicted using mirDIP. We utilized GSE42568, GSE185645, and TCGA and identified 159 common DE-mRNAs. Using Cytoscape plug-ins, we identified the 10 most significant DE-mRNAs in BC. Using mirDIP, target miRNAs for 10 DE-mRNAs were identified. We conducted an advanced analysis on the TNBC GEO data set (GSE45498) to corroborate the significance of shared DE-mRNAs and DE-miRNAs in TNBC. We identified four downregulated DE-miRNAs, including hsa-miR-802, hsa-miR-1258, hsa-miR-548a-3p, and hsa-miR-2053, significantly associated with TNBC. Our study revealed significant miRNA-mRNA interactions, specifically hsa-miR-802/MELK, hsa-miR-1258/NCAPG, miR-548a-3p/CCNA2, and hsa-miR-2053/NUSAP1, in both BC and TNBC. The observed downregulation of hsa-miR-548a-3p is associated with diminished survival rates in BC patients, emphasizing their potential utility as prognostic indicators. Furthermore, the differential expression of mRNAs, including CCNB2, UBE2C, MELK, and KIF2C, correlates with reduced survival outcomes, signifying their critical role as potential targets for therapeutic intervention in both BC and TNBC. These findings highlight specific regulatory mechanisms that are potentially crucial for understanding and treating these cancer types.

Mitochondrial-related microRNAs and their roles in cellular senescence

Aging is a natural aspect of mammalian life. Although cellular mortality is inevitable, various diseases can hasten the aging process, resulting in abnormal or premature senescence. As cells age, they experience distinctive morphological and biochemical shifts, compromising their functions. Research has illuminated that cellular senescence coincides with significant alterations in the microRNA (miRNA) expression profile. Notably, a subset of aging-associated miRNAs, originally encoded by nuclear DNA, relocate to mitochondria, manifesting a mitochondria-specific presence. Additionally, mitochondria themselves house miRNAs encoded by mitochondrial DNA (mtDNA). These mitochondria-residing miRNAs, collectively referred to as mitochondrial miRNAs (mitomiRs), have been shown to influence mtDNA transcription and protein synthesis, thereby impacting mitochondrial functionality and cellular behavior. Recent studies suggest that mitomiRs serve as critical sensors for cellular senescence, exerting control over mitochondrial homeostasis and influencing metabolic reprogramming, redox equilibrium, apoptosis, mitophagy, and calcium homeostasis-all processes intimately connected to senescence. This review synthesizes current findings on mitomiRs, their mitochondrial targets, and functions, while also exploring their involvement in cellular aging. Our goal is to shed light on the potential molecular mechanisms by which mitomiRs contribute to the aging process.

Withaferin A alters the expression of microRNAs 146a-5p and 34a-5p and associated hub genes in MDA-MB-231 cells

Triple-negative breast cancer (TNBC) is a highly metastatic subtype of breast cancer. Due to the absence of obvious therapeutic targets, microRNAs (miRNAs) provide possible hope to treat TNBC. Withaferin A (WA), a steroidal lactone, possesses potential anticancer activity with lesser side effects. The present study identifies hub genes (CDKN3, TRAF6, CCND1, JAK1, MET, AXIN2, JAG1, VEGFA, BRCA1, E2F3, WNT1, CDK6, KRAS, MYB, MYCN, TGFβR2, NOTCH1, SIRT1, MYCN, NOTCH2, WNT3A) from the list of predicted targets of the differentially expressed miRNAs (DEMs) in WA-treated MDA-MB-231 cells using in silico protein-protein interaction network analysis. CCND1, CDK6, and TRAF6 hub genes were predicted as targets of miR-34a-5p and miR-146a-5p, respectively. The study found the lower expression of miR-34a-5p and miR-146a-5p in MDA-MB-231 cells, and further, it was observed that WA treatment effectively restored the lost expression of miR-34a-5p and miR-146a-5p in MDA-MB-231 cells. An anti-correlation expression pattern was found among the miR-34a-5p and miR-146a-5p and the respective target hub genes in WA-treated TNBC cells. In conclusion, WA might exert anti-cancer effect in TNBC cells by inducing miR-34a-5p and miR-146a-5p expressions and decreasing CCND1, CDK6, and TARF6 target hub genes in TNBC cells.

Assessment of breast cytoarchitecture and its associated axillary lymph node status under normal and pathological conditions in Egyptian women

OBJECTIVE

Herein, we compare the features of neoplastic cancer cells in invasive ductal carcinoma (IDC) grade II and III patients to their corresponding normal cells both in breast and axillary lymph node (ALN) tissues.

METHODS

A retrospective cohort of 70 female breast cancer patients enrolled between 2018 and 2020 at Medical Research Institute, Alexandria University, Egypt, was analyzed for clinicopathological features presentation. Fresh tiny pieces of breast tissue and its associated ALN tissues were then processed to investigate the morphological appearance by scanning electron microscopy. Moreover, the histological architecture of tissue sections stained with hematoxylin and eosin was studied by light microscope, while the characterization of the ultrastructure features of breast and ALN tissues was analyzed by transmission electron microscopy.

RESULTS

Clinicopathological presentation of patients revealed that the Egyptian female breast cancer population adhered to the global trends of breast cancer disease with elevated incidence rate among postmenopausal women (61.3%), high frequency of IDC (95.7%), and increased ALN metastasis (65.7%). The percentage of estrogen receptor alpha (ERα) and human epidermal growth factor receptor 2 (HER2) expression, as key indicators for carcinogenesis and disease progression was 87.1% and 55.8%, respectively. The present study points to the observed discrepancies among the investigated variables in the diagnostic separation between IDC grade II and grade III. Ductal epithelial cells organization, nuclei size and irregularity, chromatin amount and uniformity, mitochondrial abundance and dysfunction were differentially manifested in IDC grades. Moreover, aberrations in the cellular organelles like lysosomes, endoplasmic reticulum, and lipid droplets vary according to the grade of IDC and the aggressiveness of the invasive breast cancer.

CONCLUSIONS

To sum up, this study emphasizes the importance of accurate specimen evaluation for treatment choice and decision.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer

BACKGROUND

The largest group of patients with breast cancer are estrogen receptor-positive (ER+) type. The estrogen receptor acts as a transcription factor and triggers cell proliferation and differentiation. Hence, investigating ER-DNA interaction genomic regions can help identify genes directly regulated by ER and understand the mechanism of ER action in cancer progression.

METHODS

In the present study, we employed a workflow to do a meta-analysis of ChIP-seq data of ER+ cell lines stimulated with 10 nM and 100 nM of E2. All publicly available data sets were re-analyzed with the same platform. Then, the known and unknown batch effects were removed. Finally, the meta-analysis was performed to obtain meta-differentially bound sites in estrogen-treated MCF7 cell lines compared to vehicles (as control). Also, the meta-analysis results were compared with the results of T47D cell lines for more precision. Enrichment analyses were also employed to find the functional importance of common meta-differentially bound sites and associated genes among both cell lines.

RESULTS

Remarkably, POU5F1B, ZNF662, ZNF442, KIN, ZNF410, and SGSM2 transcription factors were recognized in the meta-analysis but not in individual studies. Enrichment of the meta-differentially bound sites resulted in the candidacy of pathways not previously reported in breast cancer. PCGF2, HNF1B, and ZBED6 transcription factors were also predicted through the enrichment analysis of associated genes. In addition, comparing the meta-analysis results of both ChIP-seq and RNA-seq data showed that many transcription factors affected by ER were up-regulated.

CONCLUSION

The meta-analysis of ChIP-seq data of estrogen-treated MCF7 cell line leads to the identification of new binding sites of ER that have not been previously reported. Also, enrichment of the meta-differentially bound sites and their associated genes revealed new terms and pathways involved in the development of breast cancer which should be examined in future in vitro and in vivo studies.

Characterization and verification of MMP family members as potential biomarkers in kidney clear cell renal carcinoma

Renal cell carcinoma can arise from lesions in the renal epithelium. This particular type of cancer is prevalent in the realm of renal cancers and is associated with an unfavorable prognosis. Among these cases, over 70% are classified as kidney renal clear cell carcinoma (KIRC). Since the underlying causes of KIRC haven't been fully understood, there is an urgent need for deeper investigation into its pathogenesis. Various tools, software, and molecular analysis was used, including Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Cytoscape, University of ALabama at Birmingham CANcer data analysis Portal (UALCAN), muTarget, Gene Expression Profiling Interactive Analysis (GEPIA), OncoDB, Human Protein Atlas (HPA), cBioPortal, Kaplan-Meier (KM) plotter, Gene Set Enrichment Analysis (GSEA), Tumor IMmune Estimation Resource (TIMER), Encyclopedia of RNA Interactomes (ENCORI), DrugBank, Encyclopedia of RNA Interactomes (RT-qPCR), targeted bisulfide sequencing (bisulfide-seq), and receiver operating curve (ROC) to matrix metallopeptidase (MMP) gene family constituents, with the precise objective of identifying a small set of hub genes. These hub genes hold the potential to be harnessed as molecular biomarkers for KIRC. By performing STRING and CytoHubba analyses of the 24 MMP gene family members, MMP2 (matrix metallopeptidase 2), MMP9 (matrix metallopeptidase 9), MMP14 (matrix metallopeptidase 14), and MMP16 (matrix metallopeptidase 16) were recognized as hub genes having highest degree scores. After conducting an in-depth expression analysis of MMP2, MMP9, MMP14, and MMP16 using various The Cancer Genome Atlas (TCGA) databases and RT-qPCR techniques, these displayed a significant increase in expression at both the mRNA and protein levels within KIRC samples when compared to control samples. The impact of the over expression of MMP2, MMP9, MMP14, and MMP16 also left a distinct mark on the worst overall survival (OS) rates of KIRC patients. Furthermore, a targeted bisulfide-seq investigation unveiled a correlation between promoter hypomethylation patterns and the up-regulation of these key genes in KIRC patients. Additionally, hub genes were involved in various diverse oncogenic pathways. In conclusion, four MMP gene family members, including MMP2, MMP9, MMP14, and MMP16 may serve as therapeutic target and molecular biomarker in KIRC.

Nuclear mitochondria-related genes-based molecular classification and prognostic signature reveal immune landscape, somatic mutation, and prognosis for glioma

Background

Glioma is the most frequent malignant primary brain tumor, and mitochondria may influence the progression of glioma. The aim of this study was to analyze the role of nuclear mitochondria related genes (MTRGs) in glioma, identify subtypes and construct a prognostic model based on nuclear MTRGs and machine learning algorithms.

Methods

Samples containing both gene expression profiles and clinical information were retrieved from the TCGA database, CGGA database, and GEO database. We selected 16 nuclear MTRGs and identified two clusters of glioma. Prognostic features, microenvironment, mutation landscape, and drug sensitivity were compared between the clusters. A prognostic model based on multiple machine learning algorithms was then constructed and validated by multiple datasets.

Results

We observed significant discrepancies between the two clusters. Cluster One had higher nuclear MTRG expression, a lower survival rate, and higher immune infiltration than Cluster Two. For the two clusters, we found distinct predictive drug sensitivities and responses to immune therapy, and the infiltration of immune cells was significantly different. Among the 22 combinations of machine learning algorithms we tested, LASSO was the most effective in constructing the prognostic model. The model's accuracy was further verified in three independent glioma datasets. We identified MGME1 as a vital gene associated with infiltrating immune cells in multiple types of tumors.

Conclusion

In short, our research identified two clusters of glioma and developed a dependable prognostic model based on machine learning methods. MGME1 was identified as a potential biomarker for multiple tumors. Our results will contribute to precise medicine and glioma management.

A novel signature model based on mitochondrial-related genes for predicting survival of colon adenocarcinoma

Background

Colon cancer is the foremost reason of cancer-related mortality worldwide. Colon adenocarcinoma constitutes 90% of colon cancer, and most patients with colon adenocarcinoma (COAD) are identified until advanced stage. With the emergence of an increasing number of novel pathogenic mechanisms and treatments, the role of mitochondria in the development of cancer, has been studied and reported with increasing frequency.

Methods

We systematically analyzed the effect of mitochondria-related genes in COAD utilizing RNA sequencing dataset from The Cancer Genome Atlas database and 1613 mitochondrial function-related genes from MitoMiner database. Our approach consisted of differentially expressed gene, gene set enrichment analysis, gene ontology terminology, Kyoto Encyclopedia of Genes and Genomes, independent prognostic analysis, univariate and multivariate analysis, Kaplan–Meier survival analysis, immune microenvironment correlation analysis, and Cox regression analysis.

Results

Consequently, 8 genes were identified to construct 8 mitochondrial-related gene model by applying Cox regression analysis, CDC25C, KCNJ11, NOL3, P4HA1, QSOX2, Trap1, DNAJC28, and ATCAY. Meanwhile, we assessed the connection between this model and clinical parameters or immune microenvironment. Risk score was an independent predictor for COAD patients’ survival with an AUC of 0.687, 0.752 and 0.762 at 1-, 3- and 5-year in nomogram, respectively. The group with the highest risk score had the lowest survival rate and the worst clinical stages. Additionally, its predictive capacity was validated in GSE39582 cohort.

Conclusion

In summary, we established a prognostic pattern of mitochondrial-related genes, which can predict overall survival in COAD, which may enable a more optimized approach for the clinical treatment and scientific study of COAD. This gene signature model has the potential to improve prognosis and treatment for COAD patients in the future, and to be widely implemented in clinical settings. The utilization of this mitochondrial-related gene signature model may be benefit in the treatments and medical decision-making of COAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-02020-3.

Prognostic microRNAs associated with phosphoserine aminotransferase 1 in gastric cancer as markers of bone metastasis

This study analyzed PSAT1-targeted miRNAs as a prognostic predictor for gastric cancer. The relationship between the clinical manifestations of gastric cancer in patients and phosphoserine aminotransferase 1 (PSAT1) was analyzed using correlation analysis. PSAT1 was highly expressed in gastric cancer, and its low expression was associated with a poor prognosis. By pan-cancer analysis, PSAT1 could affect the tumor immune microenvironment by immune infiltration analysis. Nine microRNAs targeting PSAT1 and associated with gastric cancer were screened by miRwalk and microRNA expression in TCGA tumor tissues. Six microRNAs were obtained by survival curve analysis, including hsa-miR-1-3p, hsa-miR-139-5p, hsa-miR-145-5p, hsa-miR-195-5p, hsa-miR-218-5p, and hsa-miR-497-5p. Based on the above six microRNAs, a model for bone metastasis prediction in gastric cancer prediction was constructed. An analysis of a decision curve was performed based on the microRNAs obtained to predict bone metastasis from gastric cancer. It had a positive area under the curve (AUC) value of 0.746, and the decision curve analysis (DCA) indicated that it was clinically significant. Dual-luciferase reporter genes indicated that hsa-miR-497-5p and PSAT1 were targeted, and qRT-PCR results confirmed that hsa-miR-497-5p could down-regulate PSAT1 expression. MicroRNAs targeting the regulation of PSAT1 expression can well predict the prognosis of gastric cancer.

Transcriptome Analysis Reveals Hub Genes Regulating Autophagy in Patients With Severe COVID-19

Background: The COVID-19 pandemic has currently developed into a worldwide threat to humankind. Importantly, patients with severe COVID-19 are believed to have a higher mortality risk than those with mild conditions. However, despite the urgent need to develop novel therapeutic strategies, the biological features and pathogenic mechanisms of severe COVID-19 are poorly understood.

Methods: Here, peripheral blood mononuclear cells (PBMCs) from four patients with severe COVID-19, four patients with mild COVID-19, and four healthy controls were examined by RNA sequencing (RNA-Seq). We conducted gene expression analysis and Venn diagrams to detect specific differentially expressed genes (DEGs) in patients with severe disease compared with those with mild conditions. Gene Ontology (GO) enrichment analysis was performed to identify the significant biological processes, and protein–protein interaction networks were constructed to extract hub genes. These hub genes were then subjected to regulatory signatures and protein–chemical interaction analysis for certain regulatory checkpoints and identification of potent chemical agents. Finally, to demonstrate the cell type-specific expression of these genes, we performed single-cell RNA-Seq analyses using an online platform.

Results: A total of 144 DEGs were specifically expressed in severe COVID-19, and GO enrichment analysis revealed a significant association of these specific DEGs with autophagy. Hub genes such as MVB12A, CHMP6, STAM, and VPS37B were then found to be most significantly involved in the biological processes of autophagy at the transcriptome level. In addition, six transcription factors, including SRF, YY1, CREB1, PPARG, NFIC, and GATA2, as well as miRNAs, namely, hsa-mir-1-3p, and potent chemical agents such as copper sulfate and cobalt chloride, may cooperate in regulating the autophagy hub genes. Furthermore, classical monocytes may play a central role in severe COVID-19.

Conclusion: We suggest that autophagy plays a crucial role in severe COVID-19. This study might facilitate a more profound knowledge of the biological characteristics and progression of COVID-19 and the development of novel therapeutic approaches to achieve a breakthrough in the current COVID-19 pandemic.

Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies

Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.

KLF9 regulates miR-338-3p/NRCAM axis to block the progression of osteosarcoma cells

Background: MiR-338-3p is revealed to serve as a tumor suppressor in several carcinomas. Whereas, the effect of miR-338-3p in the progression of osteosarcoma has not been explored. The aim of this paper was to analyze the functional influences of miR-338-3p on osteosarcoma progression and the potential mechanism.

Methods: The expression of genes and miRNAs in osteosarcoma cells was assessed via western blotting or quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Osteosarcoma cellular proliferation was explored by MTT and EdU incorporation assay. Osteosarcoma cellular migratory and invasive capacity was explored by wound-healing and transwell assay. Bioinformatics approaches were adopted to predict target genes. The relationships between miR-338-3p and neuron‑glial‑related cell adhesion (NRCAM), between kruppel-like factor 9 (KLF9) and miR-338-3p were verified by dual-luciferase reporter assay.

Results: We found that miR-338-3p was reduced in osteosarcoma and that higher expression of miR-338-3p suppressed proliferative, invasive and migratory ability of osteosarcoma cells. Furthermore, the result showed that overexpression of NRCAM could reduce the anti-tumor role of miR-338-3p in osteosarcoma cells. In addition, we found that overexpression of KLF9 could enhance the expression level of miR-338-3p in osteosarcoma cells.

Conclusion: The KLF9/miR-338-3p/NRCAM axis played a significant role in regulating osteosarcoma progression, which may become a promising therapeutic method for osteosarcoma.

Global miRNA expression reveals novel nuclear and mitochondrial interactions in Type 1 diabetes mellitus

BACKGROUND

Considering the potential role of miRNAs as biomarkers and their interaction with both nuclear and mitochondrial genes, we investigated the miRNA expression profile in type 1 diabetes (T1DM) patients, including the pathways in which they are involved considering both nuclear and mitochondrial functions.

METHODS

We analyzed samples of T1DM patients and control individuals (normal glucose tolerance) by high throughput miRNA sequencing (miRNome). Next, five miRNAs - hsa-miR-26b-5p, hsa-let-7i-5p, hsa-miR-143-3p, hsa-miR-501-3p and hsa-miR-100-5p - were validated by RT-qPCR. The identification of target genes was extracted from miRTarBase and mitoXplorer database. We also performed receiver operating characteristic (ROC) curves and miRNAs that had an AUC > 0.85 were considered potential biomarkers.

RESULTS

Overall, 41 miRNAs were differentially expressed in T1DM patients compared to control. Hsa-miR-21-5p had the highest number of predicted target genes and was associated with several pathways, including insulin signaling and apoptosis. 34.1% (14/41) of the differentially expressed miRNAs also targeted mitochondrial genes, and 80.5% (33/41) of them targeted nuclear genes involved in the mitochondrial metabolism. All five validated miRNAs were upregulated in T1DM. Among them, hsa-miR-26b-5p showed AUC>0.85, being suggested as potential biomarker to T1DM.

CONCLUSION

Our results demonstrated 41 DE miRNAs that had a great accuracy in discriminating T1DM and control group. Furthermore, we demonstrate the influence of these miRNAs on numerous metabolic pathways, including mitochondrial metabolism. Hsa-miR-26b-5p and hsa-miR-21-5p were highlighted in our results, possibly acting on nuclear and mitochondrial dysfunction and, subsequently, T1DM dysregulation.

Transcriptome Analyses Identify Potential Key microRNAs and Their Target Genes Contributing to Ovarian Reserve

Female endocrinological symptoms, such as premature ovarian inefficiency (POI) are caused by diminished ovarian reserve and chemotherapy. The etiology of POI remains unknown, but this can lead to infertility. This has accelerated the search for master regulator genes or other molecules that contribute as enhancers or silencers. The impact of regulatory microRNAs (miRNAs) on POI has gained attention; however, their regulatory function in this condition is not well known. RNA sequencing was performed at four stages, 2-(2 W), 6-(6 W), 15-(15 W), and 20-(20 W) weeks, on ovarian tissue samples and 5058 differentially expressed genes (DEGs) were identified. Gene expression and enrichment were analyzed based on the gene ontology and KEGG databases, and their association with other proteins was assessed using the STRING database. Gene set enrichment analysis was performed to identify the key target genes. The DEGs were most highly enriched in 6 W and 15 W groups. Figla, GDF9, Nobox, and Pou51 were significantly in-creased at 2 W compared with levels at 6 W and 20 W, whereas the expression of Foxo1, Inha, and Taf4b was significantly de-creased at 20 W. Ccnd2 and Igf1 expression was maintained at similar levels in each stage. In total, 27 genes were upregulated and 26 genes interacted with miRNAs; moreover, stage-specific upregulated and downregulated interactions were demonstrated. Increased and decreased miRNAs were identified at each stage in the ovaries. The constitutively expressed genes, Ccnd2 and Igf1, were identified as the major targets of many miRNAs (p < 0.05), and Fshr and Foxo3 interacted with miRNAs, namely mmu-miR-670-3p and mmu-miR-153-3p. miR-26a-5p interacted with Piwil2, and its target genes were downregulated in the 20 W mouse ovary. In this study, we aimed to identify key miRNAs and their target genes encompassing the reproductive span of mouse ovaries using mRNA and miRNA sequencing. These results indicated that gene sets are regulated in the reproductive stage-specific manner via interaction with miRNAs. Furthermore, consistent expression of Ccnd2 and Igf1 is considered crucial for the ovarian reserve and is regulated by many interactive miRNAs.

MicroRNA-1: Diverse role of a small player in multiple cancers

The process of cancer initiation and development is a dynamic and complex mechanism involving multiple genetic and non-genetic variations. With the development of high throughput techniques like next-generation sequencing, the field of cancer biology extended beyond the protein-coding genes. It brought the functional role of noncoding RNAs into cancer-associated pathways. MicroRNAs (miRNAs) are one such class of noncoding RNAs regulating different cancer development aspects, including progression and metastasis. MicroRNA-1 (miR-1) is a highly conserved miRNA with a functional role in developing skeletal muscle precursor cells and cardiomyocytes and acts as a consistent tumor suppressor gene. In humans, two discrete genes, MIR-1-1 located on 20q13.333 and MIR-1-2 located on 18q11.2 loci encode for a single mature miR-1. Downregulation of miR-1 has been demonstrated in multiple cancers, including lung, breast, liver, prostate, colorectal, pancreatic, medulloblastoma, and gastric cancer. A vast number of studies have shown that miR-1 affects the hallmarks of cancer like proliferation, invasion and metastasis, apoptosis, angiogenesis, chemosensitization, and immune modulation. The potential therapeutic applications of miR-1 in multiple cancer pathways provide a novel platform for developing anticancer therapies. This review focuses on the different antitumorigenic and therapeutic aspects of miR-1, including how it regulates tumor development and associated immunomodulatory functions.