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Biology
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Translational Bioinformatics for Transcriptome Analysis in Tumor Biology
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Translational Bioinformatics for Transcriptome Analysis in Tumor Biology

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 9537

Special Issue Editors

Dr. Massimo La Rosa

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Guest Editor
CNR-ICAR, National Research Council of Italy, Rome, Italy
Interests: bionformatics; computational biology; miRNA and ceRNA interactions; machine learning and deep learning
Dr. Antonino Fiannaca

E-Mail Website
Guest Editor
National Research Council of Italy - High-Performance Computing and Networking Institute (CNR -ICAR), Italy
Interests: bioinformatics; precision medicine; machine learning; NGS sequencing; omics data analysis
Dr. Laura La Paglia

E-Mail Website
Guest Editor
National Research Council of Italy - High-Performance Computing and Networking Institute (CNR -ICAR), Italy
Interests: bioinformatics; genetics; cancer biology; molecular biology; NGS; data integration
Dr. Alfonso Urso

E-Mail Website
Guest Editor
National Research Council of Italy - High-Performance Computing and Networking Institute (CNR -ICAR), Italy
Interests: machine learning, soft computing and applications to bioinformatics

Special Issue Information

Dear Colleagues,

The investigation of the whole transcriptome landscape in cancer pathology provides opportunities to understand the relationships among different molecules as well as the regulative mechanisms among RNA networks, such as competitive endogenous RNA (ceRNA) regulative network dynamics. Translating this concept into the clinic, the understanding of neoplastic cell behavior provides opportunities to identify biomarkers that can be informative in relation to risk and in the choice of treatment options. In recent years, the availability of third-generation next generation sequencing (NGS), coupled with bioinformatics pipelines for cancer transcriptome analysis, has provided sequence data and the gene expression values of coding genes as well as other non-coding RNA molecules, such as miRNA, lncRNA, and circRNA. Moreover, bioinformatics pipelines have provided the possibility of identifying fusion transcripts that result from translocations and other structural alterations or ceRNA–ceRNA regulative networks by identifying the dynamics between different regulatory RNA molecules that play a role in driving gene expression.

The study of several RNA types allows, for example, the identification of specific biomarkers and the detection of unambiguous molecular targets associated to early signs of cancer and will be instrumental in developing new targeted therapies for cancer treatment.

This Special Issue will focus on new insights into the analysis of transcriptome data from NGS methodologies (second and third generation), using innovative or state-of-the-art bioinformatics pipelines and algorithms for bulk and single-cell sequencing, with particular insight on data integration and multi-omics approaches in the field of translational research.

Dr. Massimo La Rosa
Dr. Antonino Fiannaca
Dr. Laura La Paglia
Dr. Alfonso Urso
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • transcriptome
  • NGS
  • single-cell sequencing
  • bulk sequencing
  • algorithms
  • data integration
  • ncRNA
  • biological networks
  • multi-omics approach
  • functional analysis

Published Papers (3 papers)

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Research

14 pages, 3300 KiB  
Article
Identifying Complex lncRNA/Pseudogene–miRNA–mRNA Crosstalk in Hormone-Dependent Cancers
by Dulari K. Jayarathna, Miguel E. Rentería, Emilie Sauret, Jyotsna Batra and Neha S. Gandhi
Biology 2021, 10(10), 1014; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10101014 - 09 Oct 2021
Cited by 6 | Viewed by 3159
Abstract
The discovery of microRNAs (miRNAs) has fundamentally transformed our understanding of gene regulation. The competing endogenous RNA (ceRNA) hypothesis postulates that messenger RNAs and other RNA transcripts, such as long non-coding RNAs and pseudogenes, can act as natural miRNA sponges. These RNAs influence [...] Read more.
The discovery of microRNAs (miRNAs) has fundamentally transformed our understanding of gene regulation. The competing endogenous RNA (ceRNA) hypothesis postulates that messenger RNAs and other RNA transcripts, such as long non-coding RNAs and pseudogenes, can act as natural miRNA sponges. These RNAs influence each other’s expression levels by competing for the same pool of miRNAs through miRNA response elements on their target transcripts, thereby modulating gene expression and protein activity. In recent years, these ceRNA regulatory networks have gained considerable attention in cancer research. Several studies have identified cancer-specific ceRNA networks. Nevertheless, prior bioinformatic analyses have focused on long-non-coding RNA-associated ceRNA networks. Here, we identify an extended ceRNA network (including both long non-coding RNAs and pseudogenes) shared across a group of five hormone-dependent (HD) cancers, i.e., prostate, breast, colon, rectal, and endometrial cancers, using data from The Cancer Genome Atlas (TCGA). We performed a functional enrichment analysis for differentially expressed genes in the shared ceRNA network of HD cancers, followed by a survival analysis to determine their prognostic ability. We identified two long non-coding RNAs, nine genes, and seventy-four miRNAs in the shared ceRNA network across five HD cancers. Among them, two genes and forty-one miRNAs were associated with at least one HD cancer survival. This study is the first to investigate pseudogene-associated ceRNAs across a group of related cancers and highlights the value of this approach to understanding the shared molecular pathogenesis in a group of related diseases. Full article
(This article belongs to the Special Issue Translational Bioinformatics for Transcriptome Analysis in Tumor Biology)
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12 pages, 9865 KiB  
Article
Integration of Multiple Resolution Data in 3D Chromatin Reconstruction Using ChromStruct
by Claudia Caudai, Monica Zoppè, Anna Tonazzini, Ivan Merelli and Emanuele Salerno
Biology 2021, 10(4), 338; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10040338 - 16 Apr 2021
Cited by 2 | Viewed by 2200
Abstract
The three-dimensional structure of chromatin in the cellular nucleus carries important information that is connected to physiological and pathological correlates and dysfunctional cell behaviour. As direct observation is not feasible at present, on one side, several experimental techniques have been developed to provide [...] Read more.
The three-dimensional structure of chromatin in the cellular nucleus carries important information that is connected to physiological and pathological correlates and dysfunctional cell behaviour. As direct observation is not feasible at present, on one side, several experimental techniques have been developed to provide information on the spatial organization of the DNA in the cell; on the other side, several computational methods have been developed to elaborate experimental data and infer 3D chromatin conformations. The most relevant experimental methods are Chromosome Conformation Capture and its derivatives, chromatin immunoprecipitation and sequencing techniques (CHIP-seq), RNA-seq, fluorescence in situ hybridization (FISH) and other genetic and biochemical techniques. All of them provide important and complementary information that relate to the three-dimensional organization of chromatin. However, these techniques employ very different experimental protocols and provide information that is not easily integrated, due to different contexts and different resolutions. Here, we present an open-source tool, which is an expansion of the previously reported code ChromStruct, for inferring the 3D structure of chromatin that, by exploiting a multilevel approach, allows an easy integration of information derived from different experimental protocols and referred to different resolution levels of the structure, from a few kilobases up to Megabases. Our results show that the introduction of chromatin modelling features related to CTCF CHIA-PET data, histone modification CHIP-seq, and RNA-seq data produce appreciable improvements in ChromStruct’s 3D reconstructions, compared to the use of HI-C data alone, at a local level and at a very high resolution. Full article
(This article belongs to the Special Issue Translational Bioinformatics for Transcriptome Analysis in Tumor Biology)
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Figure 1

26 pages, 9394 KiB  
Article
Prognostic and Functional Significant of Heat Shock Proteins (HSPs) in Breast Cancer Unveiled by Multi-Omics Approaches
by Miriam Buttacavoli, Gianluca Di Cara, Cesare D’Amico, Fabiana Geraci, Ida Pucci-Minafra, Salvatore Feo and Patrizia Cancemi
Biology 2021, 10(3), 247; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10030247 - 22 Mar 2021
Cited by 11 | Viewed by 2950
Abstract
Heat shock proteins (HSPs) are a well-characterized molecular chaperones protein family, classified into six major families, according to their molecular size. A wide range of tumors have been shown to express atypical levels of one or more HSPs, suggesting that they could be [...] Read more.
Heat shock proteins (HSPs) are a well-characterized molecular chaperones protein family, classified into six major families, according to their molecular size. A wide range of tumors have been shown to express atypical levels of one or more HSPs, suggesting that they could be used as biomarkers. However, the collective role and the possible coordination of HSP members, as well as the prognostic significance and the functional implications of their deregulated expression in breast cancer (BC) are poorly investigated. Here, we used a systematic multi-omics approach to assess the HSPs expression, the prognostic value, and the underlying mechanisms of tumorigenesis in BC. By using data mining, we showed that several HSPs were deregulated in BC and significantly correlated with a poor or good prognosis. Functional network analysis of HSPs co-expressed genes and miRNAs highlighted their regulatory effects on several biological pathways involved in cancer progression. In particular, these pathways concerned cell cycle and DNA replication for the HSPs co-expressed genes, and miRNAs up-regulated in poor prognosis and Epithelial to Mesenchymal Transition (ETM), as well as receptors-mediated signaling for the HSPs co-expressed genes up-regulated in good prognosis. Furthermore, the proteomic expression of HSPs in a large sample-set of breast cancer tissues revealed much more complexity in their roles in BC and showed that their expression is quite variable among patients and confined into different cellular compartments. In conclusion, integrative analysis of multi-omics data revealed the distinct impact of several HSPs members in BC progression and indicate that collectively they could be useful as biomarkers and therapeutic targets for BC management. Full article
(This article belongs to the Special Issue Translational Bioinformatics for Transcriptome Analysis in Tumor Biology)
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