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Bioinformatics in Genetic Diseases and Cancer

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biophysics".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 14339

Special Issue Editors

Department of Computing, Data Analytics Lab, Macquarie University, Sydney, NSW 2109, Australia
Interests: artificial intelligence; machine learning; health data analytics
Special Issues, Collections and Topics in MDPI journals
BioMedical Machine Learning Lab (BML), UNSW Sydney, Sydney, NSW 2052, Australia
Interests: artificial intelligence; multiomics; machine learning; genetic variation; cancer; genetic diseases

Special Issue Information

Dear Colleagues, 

The last decade was amazing in the identification of responsible biomedical markers associated to diseases and cancers; however, with the increasing development of new diseases and their associated data, it is required to develop new methods to discover novel biomarkers and new medicines to cure them. Applications of artifical intelligence (AI) and integrative data analytic techniques in biological complex and big data has emerged as an important field which shown its huge impact in biomedical research domains. This special issue will focus on the application of AI and data analytci techniques in particular for underestanding and identyfing novel biomedical markers, in particular genetic biomarkers in genetics diseases and cancers, this include but not limited to the topics shown below:

  • Machine learning or integrative data analytics in computational biomarker discovery in genetic diseases and disorders
  • Clinical machine learning and translational medicine in genetic diseases and disorders
  • Deep learning and data analytic techniques to uncover novel patterns and biomarkers in genetic diseases and disorders
  • Supercomputing to identify genetic biomarkers
  • Mathematical methods for diagnostic classifiers
  • Biological networks to analyse genetic variants
  • Please kindly note: New methods must be compared to existing state-of-the-art methods, using real biological data. The inclusion of experimental data is very much encouraged.

Prof. Dr. Amin Beheshti
Dr. Hamid Alinejad-Rokny
Guest Editors

Manuscript Submission Information

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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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • artificial intelligence
  • biomarker discovery
  • multi omics
  • machine learning
  • genetic variation
  • cancer
  • genetic diseases

Published Papers (8 papers)

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22 pages, 3786 KiB  
Article
Computational and Experimental Evaluation of the Immune Response of Neoantigens for Personalized Vaccine Design
by Iker Malaina, Lorena Gonzalez-Melero, Luis Martínez, Aiala Salvador, Ana Sanchez-Diez, Aintzane Asumendi, Javier Margareto, Jose Carrasco-Pujante, Leire Legarreta, María Asunción García, Martín Blas Pérez-Pinilla, Rosa Izu, Ildefonso Martínez de la Fuente, Manoli Igartua, Santos Alonso, Rosa Maria Hernandez and María Dolores Boyano
Int. J. Mol. Sci. 2023, 24(10), 9024; https://doi.org/10.3390/ijms24109024 - 19 May 2023
Cited by 1 | Viewed by 1326
Abstract
In the last few years, the importance of neoantigens in the development of personalized antitumor vaccines has increased remarkably. In order to study whether bioinformatic tools are effective in detecting neoantigens that generate an immune response, DNA samples from patients with cutaneous melanoma [...] Read more.
In the last few years, the importance of neoantigens in the development of personalized antitumor vaccines has increased remarkably. In order to study whether bioinformatic tools are effective in detecting neoantigens that generate an immune response, DNA samples from patients with cutaneous melanoma in different stages were obtained, resulting in a total of 6048 potential neoantigens gathered. Thereafter, the immunological responses generated by some of those neoantigens ex vivo were tested, using a vaccine designed by a new optimization approach and encapsulated in nanoparticles. Our bioinformatic analysis indicated that no differences were found between the number of neoantigens and that of non-mutated sequences detected as potential binders by IEDB tools. However, those tools were able to highlight neoantigens over non-mutated peptides in HLA-II recognition (p-value 0.03). However, neither HLA-I binding affinity (p-value 0.08) nor Class I immunogenicity values (p-value 0.96) indicated significant differences for the latter parameters. Subsequently, the new vaccine, using aggregative functions and combinatorial optimization, was designed. The six best neoantigens were selected and formulated into two nanoparticles, with which the immune response ex vivo was evaluated, demonstrating a specific activation of the immune response. This study reinforces the use of bioinformatic tools in vaccine development, as their usefulness is proven both in silico and ex vivo. Full article
(This article belongs to the Special Issue Bioinformatics in Genetic Diseases and Cancer)
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16 pages, 5396 KiB  
Article
Evaluation of Matrix Metalloproteases by Artificial Intelligence Techniques in Negative Biopsies as New Diagnostic Strategy in Prostate Cancer
by Noemi Eiro, Antonio Medina, Luis O. Gonzalez, Maria Fraile, Ana Palacios, Safwan Escaf, Jesús M. Fernández-Gómez and Francisco J. Vizoso
Int. J. Mol. Sci. 2023, 24(8), 7022; https://doi.org/10.3390/ijms24087022 - 10 Apr 2023
Cited by 4 | Viewed by 1304
Abstract
Usually, after an abnormal level of serum prostate-specific antigen (PSA) or digital rectal exam, men undergo a prostate needle biopsy. However, the traditional sextant technique misses 15–46% of cancers. At present, there are problems regarding disease diagnosis/prognosis, especially in patients’ classification, because the [...] Read more.
Usually, after an abnormal level of serum prostate-specific antigen (PSA) or digital rectal exam, men undergo a prostate needle biopsy. However, the traditional sextant technique misses 15–46% of cancers. At present, there are problems regarding disease diagnosis/prognosis, especially in patients’ classification, because the information to be handled is complex and challenging to process. Matrix metalloproteases (MMPs) have high expression by prostate cancer (PCa) compared with benign prostate tissues. To assess the possible contribution to the diagnosis of PCa, we evaluated the expression of several MMPs in prostate tissues before and after PCa diagnosis using machine learning, classifiers, and supervised algorithms. A retrospective study was conducted on 29 patients diagnosed with PCa with previous benign needle biopsies, 45 patients with benign prostatic hyperplasia (BHP), and 18 patients with high-grade prostatic intraepithelial neoplasia (HGPIN). An immunohistochemical study was performed on tissue samples from tumor and non-tumor areas using specific antibodies against MMP -2, 9, 11, and 13, and the tissue inhibitor of MMPs -3 (TIMP-3), and the protein expression by different cell types was analyzed to which several automatic learning techniques have been applied. Compared with BHP or HGPIN specimens, epithelial cells (ECs) and fibroblasts from benign prostate biopsies before the diagnosis of PCa showed a significantly higher expression of MMPs and TIMP-3. Machine learning techniques provide a differentiable classification between these patients, with greater than 95% accuracy, considering ECs, being slightly lower when considering fibroblasts. In addition, evolutionary changes were found in paired tissues from benign biopsy to prostatectomy specimens in the same patient. Thus, ECs from the tumor zone from prostatectomy showed higher expressions of MMPs and TIMP-3 compared to ECs of the corresponding zone from the benign biopsy. Similar differences were found for expressions of MMP-9 and TIMP-3, between fibroblasts from these zones. The classifiers have determined that patients with benign prostate biopsies before the diagnosis of PCa showed a high MMPs/TIMP-3 expression by ECs, so in the zone without future cancer development as in the zone with future tumor, compared with biopsy samples from patients with BPH or HGPIN. Expression of MMP -2, 9, 11, and 13, and TIMP-3 phenotypically define ECs associated with future tumor development. Also, the results suggest that MMPs/TIMPs expression in biopsy tissues may reflect evolutionary changes from prostate benign tissues to PCa. Thus, these findings in combination with other parameters might contribute to improving the suspicion of PCa diagnosis. Full article
(This article belongs to the Special Issue Bioinformatics in Genetic Diseases and Cancer)
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13 pages, 3843 KiB  
Article
Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes
by Suyeon Lee, Heewon Jung, Jiwoo Park and Jaegyoon Ahn
Int. J. Mol. Sci. 2023, 24(7), 6445; https://doi.org/10.3390/ijms24076445 - 29 Mar 2023
Viewed by 1240
Abstract
Accurate prediction of the prognoses of cancer patients and identification of prognostic biomarkers are both important for the improved treatment of cancer patients, in addition to enhanced anticancer drugs. Many previous bioinformatic studies have been carried out to achieve this goal; however, there [...] Read more.
Accurate prediction of the prognoses of cancer patients and identification of prognostic biomarkers are both important for the improved treatment of cancer patients, in addition to enhanced anticancer drugs. Many previous bioinformatic studies have been carried out to achieve this goal; however, there remains room for improvement in terms of accuracy. In this study, we demonstrated that patient-specific cancer driver genes could be used to predict cancer prognoses more accurately. To identify patient-specific cancer driver genes, we first generated patient-specific gene networks before using modified PageRank to generate feature vectors that represented the impacts genes had on the patient-specific gene network. Subsequently, the feature vectors of the good and poor prognosis groups were used to train the deep feedforward network. For the 11 cancer types in the TCGA data, the proposed method showed a significantly better prediction performance than the existing state-of-the-art methods for three cancer types (BRCA, CESC and PAAD), better performance for five cancer types (COAD, ESCA, HNSC, KIRC and STAD), and a similar or slightly worse performance for the remaining three cancer types (BLCA, LIHC and LUAD). Furthermore, the case study for the identified breast cancer and cervical squamous cell carcinoma prognostic genes and their subnetworks included several pathways associated with the progression of breast cancer and cervical squamous cell carcinoma. These results suggested that heterogeneous cancer driver information may be associated with cancer prognosis. Full article
(This article belongs to the Special Issue Bioinformatics in Genetic Diseases and Cancer)
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11 pages, 1505 KiB  
Article
Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes
by Hye Min Ju, Byung-Chul Kim, Ilhan Lim, Byung Hyun Byun and Sang-Keun Woo
Int. J. Mol. Sci. 2023, 24(3), 2794; https://doi.org/10.3390/ijms24032794 - 01 Feb 2023
Viewed by 1343
Abstract
This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) image features of NSCLC patients. RNA-sequencing data and 18F-FDG PET images of 53 patients [...] Read more.
This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) image features of NSCLC patients. RNA-sequencing data and 18F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value < 0.05). Nine genes with high protein–protein interaction and area under the curve (AUC) were identified as hub genes involved in the proliferation function, which plays an important role in distant recurrence of cancer. Four image features (GLRLM_SRHGE, GLRLM_HGRE, SUVmean, and GLZLM_GLNU) and six genes were identified to be correlated (p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker. Full article
(This article belongs to the Special Issue Bioinformatics in Genetic Diseases and Cancer)
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18 pages, 3267 KiB  
Article
A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants
by Mahdieh Labani, Amin Beheshti, Ahmadreza Argha and Hamid Alinejad-Rokny
Int. J. Mol. Sci. 2023, 24(3), 2472; https://doi.org/10.3390/ijms24032472 - 27 Jan 2023
Viewed by 1813
Abstract
Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and progression of PC. This highlights the importance of identifying functional [...] Read more.
Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and progression of PC. This highlights the importance of identifying functional genomic variants for early detection in high-risk PC individuals. Great efforts have been made to identify common protein-coding genetic variations; however, the impact of non-coding variations, including regulatory genetic variants, is not well understood. Identification of these variants and the underlying target genes will be a key step in improving the detection and treatment of PC. To gain an understanding of the functional impact of genetic variants, and in particular, regulatory variants in PC, we developed an integrative pipeline (AGV) that uses whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants could affect 131 coding and non-coding genes. Interestingly, our study identified the 131 protein-coding genes that are involved in disease-related pathways, including Reactome and MSigDB, for most of which targeted treatment options are currently available. Notably, our analysis revealed several non-coding RNAs, including RP11-136K7.2 and RAMP2-AS1, as potential enhancer elements of the protein-coding genes CDH12 and EZH1, respectively. Our results provide a comprehensive map of genomic variants in PC and reveal their potential contribution to prostate cancer progression and development. Full article
(This article belongs to the Special Issue Bioinformatics in Genetic Diseases and Cancer)
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9 pages, 714 KiB  
Article
KARAJ: An Efficient Adaptive Multi-Processor Tool to Streamline Genomic and Transcriptomic Sequence Data Acquisition
by Mahdieh Labani, Amin Beheshti, Nigel H. Lovell, Hamid Alinejad-Rokny and Ali Afrasiabi
Int. J. Mol. Sci. 2022, 23(22), 14418; https://doi.org/10.3390/ijms232214418 - 20 Nov 2022
Cited by 2 | Viewed by 2049
Abstract
Here we developed KARAJ, a fast and flexible Linux command-line tool to automate the end-to-end process of querying and downloading a wide range of genomic and transcriptomic sequence data types. The input to KARAJ is a list of PMCIDs or publication URLs [...] Read more.
Here we developed KARAJ, a fast and flexible Linux command-line tool to automate the end-to-end process of querying and downloading a wide range of genomic and transcriptomic sequence data types. The input to KARAJ is a list of PMCIDs or publication URLs or various types of accession numbers to automate four tasks as follows; firstly, it provides a summary list of accessible datasets generated by or used in these scientific articles, enabling users to select appropriate datasets; secondly, KARAJ calculates the size of files that users want to download and confirms the availability of adequate space on the local disk; thirdly, it generates a metadata table containing sample information and the experimental design of the corresponding study; and lastly, it enables users to download supplementary data tables attached to publications. Further, KARAJ provides a parallel downloading framework powered by Aspera connect which reduces the downloading time significantly. Full article
(This article belongs to the Special Issue Bioinformatics in Genetic Diseases and Cancer)
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20 pages, 5702 KiB  
Article
Predicting the Prognostic Value of POLI Expression in Different Cancers via a Machine Learning Approach
by Xuan Xu, Majid Jaberi-Douraki and Nicholas A. Wallace
Int. J. Mol. Sci. 2022, 23(15), 8571; https://doi.org/10.3390/ijms23158571 - 02 Aug 2022
Cited by 3 | Viewed by 1839
Abstract
Translesion synthesis (TLS) is a cell signaling pathway that facilitates the tolerance of replication stress. Increased TLS activity, the particularly elevated expression of TLS polymerases, has been linked to resistance to cancer chemotherapeutics and significantly altered patient outcomes. Building upon current knowledge, we [...] Read more.
Translesion synthesis (TLS) is a cell signaling pathway that facilitates the tolerance of replication stress. Increased TLS activity, the particularly elevated expression of TLS polymerases, has been linked to resistance to cancer chemotherapeutics and significantly altered patient outcomes. Building upon current knowledge, we found that the expression of one of these TLS polymerases (POLI) is associated with significant differences in cervical and pancreatic cancer survival. These data led us to hypothesize that POLI expression is associated with cancer survival more broadly. However, when cancers were grouped cancer type, POLI expression did not have a significant prognostic value. We presented a binary cancer random forest classifier using 396 genes that influence the prognostic characteristics of POLI in cervical and pancreatic cancer selected via graphical least absolute shrinkage and selection operator. The classifier was then used to cluster patients with bladder, breast, colorectal, head and neck, liver, lung, ovary, melanoma, stomach, and uterus cancer when high POLI expression was associated with worsened survival (Group I) or with improved survival (Group II). This approach allowed us to identify cancers where POLI expression is a significant prognostic factor for survival (p = 0.028 in Group I and p = 0.0059 in Group II). Multiple independent validation approaches, including the gene ontology enrichment analysis and visualization tool and network visualization support the classification scheme. The functions of the selected genes involving mitochondrial translational elongation, Wnt signaling pathway, and tumor necrosis factor-mediated signaling pathway support their association with TLS and replication stress. Our multidisciplinary approach provides a novel way of identifying tumors where increased TLS polymerase expression is associated with significant differences in cancer survival. Full article
(This article belongs to the Special Issue Bioinformatics in Genetic Diseases and Cancer)
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9 pages, 2790 KiB  
Technical Note
An Automated High-Throughput Screening (HTS) Spotter for 3D Tumor Spheroid Formation
by Mi-Hyeon Jeong, Inhee Kim, Kyunghyun Park, Bosung Ku, Dong Woo Lee, Kyoung Ryeol Park, Sang Youl Jeon and Jung Eun Kim
Int. J. Mol. Sci. 2023, 24(2), 1006; https://doi.org/10.3390/ijms24021006 - 05 Jan 2023
Viewed by 2352
Abstract
Three-dimensional (3D) culture platforms have been adopted in a high-throughput screening (HTS) system to mimic in vivo physiological microenvironments. The automated dispenser has been established commercially to enable spotting or distributing non-viscous or viscous biomaterials onto microplates. However, there are still challenges to [...] Read more.
Three-dimensional (3D) culture platforms have been adopted in a high-throughput screening (HTS) system to mimic in vivo physiological microenvironments. The automated dispenser has been established commercially to enable spotting or distributing non-viscous or viscous biomaterials onto microplates. However, there are still challenges to the precise and accurate dispensation of cells embedded in hydrogels such as Alginate- and Matrigel-extracellular matrices. We developed and improved an automated contact-free dispensing machine, the ASFA SPOTTER (V5 and V6), which is compatible with 96- and 384-pillar/well plates and 330- and 532-micropillar/well chips for the support of 3D spheroid/organoid models using bioprinting techniques. This enables the distribution of non-viscous and viscous biosamples, including chemical drugs and cancer cells, for large-scale drug screening at high speed and small volumes (20 to 4000 nanoliters) with no damage to cells. The ASFA SPOTTER (V5 and V6) utilizes a contact-free method that minimizes cross-contamination for the dispensation of encapsulated tissue cells with highly viscous scaffolds (over 70%). In particular, the SPOTTER V6 does not require a washing process and offers the advantage of almost no dead volume (defined as additional required sample volume, including a pre-shot and flushing shot for dispensing). It can be successfully applied for the achievement of an organoid culture in automation, with rapid and easy operation, as well as miniaturization for high-throughput screening. In this study, we report the advantages of the ASFA SPOTTER, which distributes standard-sized cell spots with hydrogels onto a 384-pillar/well plate with a fast dispensing speed, small-scale volume, accuracy, and precision. Full article
(This article belongs to the Special Issue Bioinformatics in Genetic Diseases and Cancer)
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