Identifying Interaction Clusters for MiRNA and MRNA Pairs in TCGA Network

MiR-30d inhibits cardiomyocytes autophagy promoting ferroptosis after myocardial infarction

BACKGROUND

The aim of this study was to investigate the effect of microRNA-30d (miR-30d) on autophagy and reveal the mechanism of autophagy promoting ferroptosis in H9C2 cells.

METHODS

First, we detected miR-30d expression of myocardial tissue in the sham and myocardial infarction (MI) group, and then analyzed by biochemical analysis and luciferase Genetic experiments to confirm its downstream target gene of. After using Lentivirus-ATG5 (LV-sh-ATG5) to effectively inhibit autophagy, in order to further clarify the possible mechanism of autophagy leading to ferroptosis in H9C2 cells, we have tested the relevant indicators ferroptosis.

RESULTS

We first found that miR-30d expression was down-regulated in myocardial tissue after MI, while autophagy increased, and autophagy was reduced when miR-30d was overexpressed, and then analyzed by biochemical analysis and luciferase Genetic experiments confirmed that ATG5 was a downstream target gene of miR-30d. After using Lentivirus-ATG5 (LV-shATG5) to effectively inhibit autophagy and up-regulate the expression of FTH1 and GSH peroxidase (GPX4) in H9C2 cells, reduce the content of MDA, increase the content of GSH, and increase the activity of GPX4, suggesting that autophagy after MI may promote ferroptosis in H9C2 cells.

CONCLUSIONS

The expression of miR-30d decreased in cardiomyocytes after MI and which can inhibit autophagy by binding to ATG5. Furthermore, autophagy after MI may promote ferroptosis.

Characterization of tumor suppressors and oncogenes evaluated from TCGA cancers

Mutations in oncogenes and tumor suppressor genes can significantly impact cellular function during cancer development. A comprehensive analysis of their mutation patterns and significant gene ontology terms can provide insights into cancer emergence and suggest potential targets for drug development. This study analyzes twelve cancer subtypes by focusing on significant genetic and molecular factors. Two common genetic mutations associated with cancer are single nucleotide variants (SNVs) and copy number alterations (CNAs). Oncogenes, derived from mutated proto-oncogenes, disrupt normal cell functions and promote cancer, while tumor suppressor genes, often inactivated by mutations, regulate cell processes like proliferation and DNA damage response. This study analyzed datasets from The Cancer Genome Atlas (TCGA), which provides extensive genomic data across various cancers. In our analysis results, many genes with significant p-values based on Kaplan Meier gene expression data were identified in eight cancers (BRCA, BLCA, HNSC, KIRC, LUAD, KIRP, LUSC, STAD). Moreover, STAD is the only cancer for genes with both significant p-values and functional terms reported. Interestingly, we found that LIHC was the cancer reported with only one CNA mutated gene and its survival plot p-value being significant. Additionally, KICH has no reported significant genes at all. Our study proposed the relationship between tumor suppressor and oncogenes and shed light on cancer tumorigenesis due to genetic mutations.

Insight into NSCLC through novel analysis of gene interactions and characteristics

Around 80 to 85% of all lung cancers are non-small cell lung cancer (NSCLC). Previous research has aimed at exploring the genetic basis of NSCLC through individual approaches, but studies have yet to investigate the results of combining them. Here we show that analyzing NSCLC genetics through three approaches simultaneously creates unique insights into our understanding of the disease. Through a combination of previous research and bioinformatics tools, we determined 35 NSCLC candidate genes. We analyzed these genes in 3 different approaches. First, we found the gene fusions between these candidate genes. Second, we found the common superfamilies between genes. Finally, we identified mutational signatures that are possibly associated with NSCLC. Each approach has its individual, unique results. Fusion relationships identify specific gene fusion targets, common superfamilies identify possible avenues to determine novel target genes, and identifying NSCLC associated mutational signatures has diagnostic and prognostic benefits. Combining the approaches, we found that gene CD74 has significant fusion relationships, but it has no association with the other two approaches, suggesting that CD74 is associated with NSCLC mainly because of its fusion relationships. Targeting the gene fusions of CD74 may be an alternative NSCLC treatment. This genetic analysis has indeed created unique insight into NSCLC genes. Both the results from each of the approaches separately and combined allow pursuit of more effective treatment strategies for this cancer. The methodology presented can also apply to other cancers, creating insights that current analytical methods could not find.

Berberine inhibits carcinogenesis through antagonizing the ATX-LPA-LPAR2-p38-leptin axis in a mouse hepatoma model

Chemoprevention of hepatocellular carcinoma (HCC) is highly desirable in clinic. Berberine (BBR) is reported to play potential roles in cancer treatment and prevention. We studied the chemopreventive effect of BBR on hepatocellular carcinogenesis in an inflammation-driven mouse model, as it was enriched in liver after oral administration. Oral BBR significantly decreased the number and volume of visible nodular tumors, and prolonged the median overall survival by 9 and 8 weeks in the diethylnitrosamine (DEN)-injected male and female mice respectively. The nodular tumors were induced through activation of the lysophosphatidic acid (LPA) pathway in liver. LPA stimulated the abnormal leptin transcription through interacting with LPA receptor-2 (LPAR2) followed by p38 activation, and BBR inhibited carcinogenesis by suppressing the bioactivity of LPA. Specifically, BBR significantly reduced the expression of the LPA synthetase autotaxin (ATX) and LPAR2 in the nodular tumors of DEN-injected mice. Subsequently, BBR repressed the abnormal transcription of leptin stimulated by LPA-induced phosphorylation of p38 in hepatoma cells. In fact, BBR reduced the abnormal expression of leptin in livers of DEN-injected male mice throughout the course of an 8-month experiment. BBR might be a preventive agent for HCC, working at least partially through antagonizing the ATX-LPA-LPAR2-p38-leptin axis in liver.

Characterizing the extracellular matrix transcriptome of cervical, endometrial, and uterine cancers

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The matrisome plays a critical role in the progression of cancer, but the matrisomes of gynecological cancers have not been well characterized.

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We built an in silico analysis pipeline to analyze publicly available bulk RNA-seq datasets of cervical, endometrial, and uterine cancers.

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Using a machine learning approach, we identified genes and gene networks that held inferential significance for cancer stage and patient survival.

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Cervical, endometrial, and uterine cancers are highly distinct from one another and should be analyzed separately.

Increasingly, the matrisome, a set of proteins that form the core of the extracellular matrix (ECM) or are closely associated with it, has been demonstrated to play a key role in tumor progression. However, in the context of gynecological cancers, the matrisome has not been well characterized. A holistic, yet targeted, exploration of the tumor microenvironment is critical for better understanding the progression of gynecological cancers, identifying key biomarkers for cancer progression, establishing the role of gene expression in patient survival, and for assisting in the development of new targeted therapies. In this work, we explored the matrisome gene expression profiles of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS) using publicly available RNA-seq data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) portal. We hypothesized that the matrisomal expression patterns of CESC, UCEC, and UCS would be highly distinct with respect to genes which are differentially expressed and hold inferential significance with respect to tumor progression, patient survival, or both. Through a combination of statistical and machine learning analysis techniques, we identified sets of genes and gene networks which characterized each of the gynecological cancer cohorts. Our findings demonstrate that the matrisome is critical for characterizing gynecological cancers and transcriptomic mechanisms of cancer progression and outcome. Furthermore, while the goal of pan-cancer transcriptional analyses is often to highlight the shared attributes of these cancer types, we demonstrate that they are highly distinct diseases which require separate analysis, modeling, and treatment approaches. In future studies, matrisome genes and gene ontology terms that were identified as holding inferential significance for cancer stage and patient survival can be evaluated as potential drug targets and incorporated into in vitro models of disease.

A holistic miRNA-mRNA module discovery

The regulatory role of the Micro-RNAs (miRNAs) in the messenger RNAs (mRNAs) gene expression is well understood by the biologists since some decades, even though the delving into specific aspects is in progress. In this paper we will focus on miRNA-mRNA modules, where regulation jointly occurs in miRNA-mRNA pairs. Namely, we propose a holistic procedure to identify miRNA-mRNA modules within a population of candidate pairs. Since current methods still leave open issues, we adopt the strategy of postponing any decision on the value of the module ingredients exactly at the end, i.e. at the moment of biologically exploiting the results. This diverts chains of statistical tests into sequences of specially-devised-evolving metrics on the possible solutions. This strategy is rather expensive under a computational perspective, so needing implementations on HPC. The reward stands in the discovery of new modules, possibly hosting non differentially expressed miRNAs and mRNAs and pairs containing genes that currently are considered not targeted. In the paper we implement the procedure on a Multiple Myeloma dataset publicly available on GEO platform, as a template of a cancer instance analysis, and hazard some biological issues. These results, jointly with the normal manageability of the computations, suggest that the discovery procedure may be profitably extended to a wide spectrum of diseases where miRNA-mRNA interactions play a relevant role.

Comparative expression analysis of tRF-3001a and tRF-1003 with corresponding miRNAs (miR-1260a and miR-4521) and their network analysis with breast cancer biomarkers

BACKGROUND

MicroRNAs and tRFs (tRNA-derived fragments) are small non-coding RNAs that are promising breast cancer (BC) biomarkers. miRNA sequences are found within tRFs. For example, miR-1260a and miR-4521 sequences are found within tRF-3001a and tRF-1003, respectively. No study has addressed the biomarker potential of these tRF-miRNA pairs in BC or their association with other BC miRNA biomarkers.

METHODS AND RESULTS

Real-time PCR was performed to examine the expression of miR-1260a-tRF-3001a and miR-4521-tRF-1003 pairs in plasma of BC patients. miR-4521 and miR-1260a showed no change in plasma of breast cancer patients (n = 19). On the contrary, both the corresponding tRFs (tRF-1003 and tRF-3001a) were down-regulated. Also, we performed miRNA/mRNA network analysis for miR-1260a and miR-4521 with top degree BC biomarkers miR-16-5p and miR-93-5p. We found that they shared nine target genes. Moreover, miR-16-5p was down-regulated, and miR-93-5p was up-regulated in the same sample set. Survival analysis plotted using clinical data from Kaplan-Meier Plotter showed that all four miRNAs and 8/9 target gene expressions could predict the survival of BC patients.

CONCLUSIONS

Our cohort analyses suggest that tRF-3001a and tRF-1003 serve as better biomarkers than their miRNA counterparts in addition to miR-93-5p and miR-16-5p. Also, they form a significant miRNA/mRNA biomarker cluster.

Morphing projections: a new visual technique for fast and interactive large-scale analysis of biomedical datasets

MOTIVATION

Biomedical research entails analyzing high dimensional records of biomedical features with hundreds or thousands of samples each. This often involves using also complementary clinical metadata, as well as a broad user domain knowledge. Common data analytics software makes use of machine learning algorithms or data visualization tools. However, they are frequently one-way analyses, providing little room for the user to reconfigure the steps in light of the observed results. In other cases, reconfigurations involve large latencies, requiring a retraining of algorithms or a large pipeline of actions. The complex and multiway nature of the problem, nonetheless, suggests that user interaction feedback is a key element to boost the cognitive process of analysis, and must be both broad and fluid.

RESULTS

In this article, we present a technique for biomedical data analytics, based on blending meaningful views in an efficient manner, allowing to provide a natural smooth way to transition among different but complementary representations of data and knowledge. Our hypothesis is that the confluence of diverse complementary information from different domains on a highly interactive interface allows the user to discover relevant relationships or generate new hypotheses to be investigated by other means. We illustrate the potential of this approach with three case studies involving gene expression data and clinical metadata, as representative examples of high dimensional, multidomain, biomedical data.

AVAILABILITY AND IMPLEMENTATION

Code and demo app to reproduce the results available at https://gitlab.com/idiazblanco/morphing-projections-demo-and-dataset-preparation.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational Identification of Sex-Biased Biomarker MicroRNAs and Genes Associated with Immune Infiltration in Breast Cancer

MicroRNAs (miRNAs) perform their functions through targeting messenger RNAs (mRNAs). X chromosome-located (X-linked) miRNAs have a broad role in cell lineage determination, immune regulation, and oncogenesis. The regulating roles of miRNAs in cancer and immunity are often altered when aberrant expression happens. Sex-biased genes could contribute to cancer sex bias in the context of their expression change due to targeting miRNAs. How biological roles and associations with immune cell abundance levels for sex-biased gene-miRNA pairs in gender-related cancer (e.g., breast cancer) change due to the alteration of their expression pattern to identify candidate therapeutic markers has not been investigated thoroughly. Upon analyzing anti-correlated genes and miRNAs within significant clusters of 12 The Cancer Genome Atlas (TCGA) cancer types and the list of sex-biased genes and miRNAs reported from previous studies, 125 sex-biased genes (11 male-biased and 114 female-biased) were identified in breast cancer (BC). Seventy-three sex-biased miRNAs (40 male-biased and 33 female-biased) were identified across 5 out of 12 cancers (head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), and lung adenocarcinoma (LUAD)). Correlation between the BC sex-biased genes and tumor infiltrating immune cell types was further evaluated. We found eight genes having high correlation with immune infiltration. Fifteen candidate female-biased BC genes targeted by 3 X-linked miRNAs (has-mir-18hashsa-mir-221, and hsa-mir-224) were pinpointed in this study. Our computational result indicates that many identified female-biased genes which have positive associations with immune cell abundance levels could serve as alternative therapeutic markers. Our analysis suggests that female-biased expression of BC candidate genes is likely influenced by their targeting miRNA(s).

Pinpointing miRNA and genes enrichment over trait-relevant tissue network in Genome-Wide Association Studies

BACKGROUND

Understanding gene regulation is important but difficult. Elucidating tissue-specific gene regulation mechanism is even more challenging and requires gene co-expression network assembled from protein-protein interaction, transcription factor and gene binding, and post-transcriptional regulation (e.g., miRNA targeting) information. The miRNA binding affinity could therefore be changed by SNP(s) located at the 3' untranslated regions (3'UTR) of the target messenger RNA (mRNA) which miRNA(s) interacts with. Genome-wide association study (GWAS) has reported significant numbers of loci hosting SNPs associated with many traits. The goal of this study is to pinpoint GWAS functional variants located in 3'UTRs and elucidate if the genes harboring these variants along with their targeting miRNAs are associated with genetic traits relevant to certain tissues.

METHODS

By applying MIGWAS, CoCoNet, ANNOVAR, and DAVID bioinformatics software and utilizing the gene expression database (e.g. GTEx data) to study GWAS summary statistics for 43 traits from 28 GWAS studies, we have identified a list of miRNAs and targeted genes harboring 3'UTR variants, which could contribute to trait-relevant tissue over miRNA-target gene network.

RESULTS

Our result demonstrated that strong association between traits and tissues exists, and in particular, the Primary Biliary Cirrhosis (PBC) trait has the most significant p-value for all 180 tissues among all 43 traits used for this study. We reported SNPs located in 3'UTR regions of genes (SFMBT2, ZC3HAV1, and UGT3A1) targeted by miRNAs for PBC trait and its tissue association network. After employing Gene Ontology (GO) analysis for PBC trait, we have also identified a very important miRNA targeted gene over miRNA-target gene network, PFKL, which encodes the liver subunit of an enzyme.

CONCLUSIONS

The non-coding variants identified from GWAS studies are casually assumed to be not critical to translated protein product. However, 3' untranslated regions (3'UTRs) of genes harbor variants can often change the binding affinity of targeting miRNAs playing important roles in protein translation degree. Our study has shown that GWAS variants could play important roles on miRNA-target gene networks by contributing the association between traits and tissues. Our analysis expands our knowledge on trait-relevant tissue network and paves way for future human disease studies.

Identification of MicroRNA-Related Tumorigenesis Variants and Genes in the Cancer Genome Atlas (TCGA) Data

MicroRNAs (miRNAs) are a class of small non-coding RNA that can down-regulate their targets by selectively binding to the 3′ untranslated region (3′UTR) of most messenger RNAs (mRNAs) in the human genome. Single nucleotide variants (SNVs) located in miRNA target sites (MTS) can disrupt the binding of targeting miRNAs. Anti-correlated miRNA–mRNA pairs between normal and tumor tissues obtained from The Cancer Genome Atlas (TCGA) can reveal important information behind these SNVs on MTS and their associated oncogenesis. In this study, using previously identified anti-correlated miRNA–mRNA pairs in 15 TCGA cancer types and publicly available variant annotation databases, namely dbNSFP (database for nonsynonymous SNPs’ functional predictions) and dbMTS (database of miRNA target site SNVs), we identified multiple functional variants and their gene products that could be associated with various types of cancers. We found two genes from dbMTS and 33 from dbNSFP that passed our stringent filtering criteria (e.g., pathogenicity). Specifically, from dbMTS, we identified 23 candidate genes, two of which (BMPR1A and XIAP) were associated with diseases that increased the risk of cancer in patients. From dbNSFP, we identified 65 variants located in 33 genes that were likely pathogenic and had a potential causative relationship with cancer. This study provides a novel way of utilizing TCGA data and integrating multiple publicly available databases to explore cancer genomics.

Innovating Computational Biology and Intelligent Medicine: ICIBM 2019 Special Issue

The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was established in 2012. The ICIBM 2019 was held from 9 to 11 June 2019 in Columbus, Ohio, USA. Out of the 105 original research manuscripts submitted to the conference, 18 were selected for publication in a Special Issue in Genes. The topics of the selected manuscripts cover a wide range of current topics in biomedical research including cancer informatics, transcriptomic, computational algorithms, visualization and tools, deep learning, and microbiome research. In this editorial, we briefly introduce each of the manuscripts and discuss their contribution to the advance of science and technology.