Bioinformatics Prediction and Analysis of MicroRNAs and Their Targets as Biomarkers for Prostate Cancer: A Preliminary Study

Role of miR-21 in the diagnosis of colorectal cancer: Meta-analysis and bioinformatics

Advanced colorectal cancer (CRC) has a bad prognosis and is challenging to cure. Therefore, there is an urgent need for an effective early diagnosis marker. MicroRNA-21 (miR-21) regulates the expression of multiple cancer target genes. The objective of this study was to assess the diagnostic role of miR-21 in CRC.A meta-analysis of PubMed, Cochrane Library, EMBASE, and Web of Science databases was performed with a carefully designed search strategy to identify records related to the diagnostic role of miR-21 in CRC. TCGA data was used to search for different microRNAs in colorectal cancer samples and surrounding tissues. In addition, potential target genes for miR-21 were predicted and evaluated by functional analysis. We conducted a meta-analysis for 10 studies, including 728 blood samples of patients with CRC and 472 healthy controls. The combined sensitivity and specificity of miR-21 to diagnose colorectal cancer were 0.79 (95% CI: 0.67-0.87) and 0.92 (95% CI: 0.85-0.96), respectively. The combined positive likelihood ratio (PLR) was 10.20 (95% CI: 4.8-21.5), the combined negative likelihood ratio (NLR) was 0.23 (95% CI: 0.14-0.37), the diagnostic odds ratio (DOR) was 45.00 (95% CI:15-132), the area under the summary receiver operating characteristic curve (SROC) for the included studies was 0.93(95%CI: 0.91-0.95). Simultaneously, TCGA data showed that miR-21 was a differential microRNA in colorectal cancer tissues and adjacent tissues, and it was an up-regulated gene. After verification by three databases, 48 target genes of miR-21 were obtained. Through GO enrichment analysis, it was found that the target genes were mainly distributed in the fiber center, the molecular function was mainly focused on cytokine receptor binding, and the biological process was mainly focused on ubiquitin-dependent protein catabolism mediated by the proteasome. KEGG pathway analysis showed that the target genes were mainly distributed in tumor pathways.

Advances in Genomic Data and Biomarkers: Revolutionizing NSCLC Diagnosis and Treatment

Non-small cell lung cancer (NSCLC) is a significant public health concern with high mortality rates. Recent advancements in genomic data, bioinformatics tools, and the utilization of biomarkers have improved the possibilities for early diagnosis, effective treatment, and follow-up in NSCLC. Biomarkers play a crucial role in precision medicine by providing measurable indicators of disease characteristics, enabling tailored treatment strategies. The integration of big data and artificial intelligence (AI) further enhances the potential for personalized medicine through advanced biomarker analysis. However, challenges remain in the impact of new biomarkers on mortality and treatment efficacy due to limited evidence. Data analysis, interpretation, and the adoption of precision medicine approaches in clinical practice pose additional challenges and emphasize the integration of biomarkers with advanced technologies such as genomic data analysis and artificial intelligence (AI), which enhance the potential of precision medicine in NSCLC. Despite these obstacles, the integration of biomarkers into precision medicine has shown promising results in NSCLC, improving patient outcomes and enabling targeted therapies. Continued research and advancements in biomarker discovery, utilization, and evidence generation are necessary to overcome these challenges and further enhance the efficacy of precision medicine. Addressing these obstacles will contribute to the continued improvement of patient outcomes in non-small cell lung cancer.

Identification and validation of metabolism-related genes signature and immune infiltration landscape of rheumatoid arthritis based on machine learning

Rheumatoid arthritis (RA) causes irreversible joint damage, but the pathogenesis is unknown. Therefore, it is crucial to identify diagnostic biomarkers of RA metabolism-related genes (MRGs). This study obtained transcriptome data from healthy individuals (HC) and RA patients from the GEO database. Weighted gene correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and random forest (RF) algorithms were adopted to identify the diagnostic feature biomarker for RA. In addition, biomarkers were verified by qRT-PCR and Western blot analysis. We established a mouse model of collagen-induced arthritis (CIA), which was confirmed by HE staining and bone structure micro-CT analysis, and then further verified the biomarkers by immunofluorescence. In vitro NMR analysis was used to analyze and identify possible metabolites. The correlation of diagnostic feature biomarkers and immune cells was performed using the Spearman-rank correlation algorithm. In this study, a total of 434 DE-MRGs were identified. GO and KEGG enrichment analysis indicated that the DE-MRGs were significantly enriched in small molecules, catabolic process, purine metabolism, carbon metabolism, and inositol phosphate metabolism. AKR1C3, MCEE, POLE4, and PFKM were identified through WGCNA, LASSO, and RF algorithms. The nomogram result should have a significant diagnostic capacity of four biomarkers in RA. Immune infiltration landscape analysis revealed a significant difference in immune cells between HC and RA groups. Our findings suggest that AKR1C3, MCEE, POLE4, and PFKM were identified as potential diagnostic feature biomarkers associated with RA's immune cell infiltrations, providing a new perspective for future research and clinical management of RA.

Regulation of Let-7a-5p and miR-199a-5p Expression by Akt1 Modulates Prostate Cancer Epithelial-to-Mesenchymal Transition via the Transforming Growth Factor-β Pathway

Simple Summary

The molecular mechanisms regulating the switch from the growth of tumor cells to invasive phenotype for metastasis is largely unknown. Molecules such as Akt1 and TGFβ have been demonstrated to play reciprocal roles in the early and advanced stages of cancers, and epithelial-to-mesenchymal transition has been identified as a common link in the process. Advancing our knowledge on the direct association between these two pathways and how their effects are reconciled in the advanced stages of cancers such as prostate cancer will have therapeutic benefits. Identifying the role of microRNAs in the process will also benefit the scientific community.

Abstract

Akt1 suppression in advanced cancers has been indicated to promote metastasis. Our understanding of how Akt1 orchestrates this is incomplete. Using the NanoString^®-based miRNA and mRNA profiling of PC3 and DU145 cells, and subsequent data analysis using the DIANA-mirPath, dbEMT, nCounter, and Ingenuity^® databases, we identified the miRNAs and associated genes responsible for Akt1-mediated prostate cancer (PCa) epithelial-to-mesenchymal transition (EMT). Akt1 loss in PC3 and DU145 cells primarily induced changes in the miRNAs and mRNAs regulating EMT genes. These include increased miR-199a-5p and decreased let-7a-5p expression associated with increased TGFβ-R1 expression. Treatment with locked nucleic acid (LNA) miR-199a-5p inhibitor and/or let-7a-5p mimic induced expression changes in EMT genes correlating to their anticipated effects on PC3 and DU145 cell motility, invasion, and TGFβ-R1 expression. A correlation between increased miR-199a-5p and TGFβ-R1 expression with reduced let-7a-5p was also observed in high Gleason score PCa patients in the cBioportal database analysis. Collectively, our studies show the effect of Akt1 suppression in advanced PCa on EMT modulating miRNA and mRNA expression changes and highlight the potential benefits of miR-199a-5p and let-7a-5p in therapy and/or early screening of mPCa.