Submit to Special Issue Submit Abstract to Special Issue Review for Genes Propose a Special Issue

Journal Browser

► Journal Browser

Special Issue "Bioinformatics and Computational Biology for Cancer Prediction and Prognosis"

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: 15 November 2023 | Viewed by 2133

Share This Special Issue

Special Issue Editors

Dr. Garrett M. Dancik
E-Mail Website
Guest Editor

Department of Computer Science, Eastern Connecticut State University, Willimantic, CT, USA
Interests: bioinformatics and computational biology; cancer bioinformatics

Dr. Spiros Vlahopoulos

E-Mail Website
Guest Editor

First Department of Pediatrics, National and Kapodistrian University of Athens, 11527 Goudi-Athens, Greece
Interests: cancer biology; leukemia
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bioinformatics tools play a vital role in understanding the biological complexity of cancer through the extraction of meaningful information from a large volume of diverse datasets. Of utmost importance are tools for data analysis, visualization, and interpretation that would aid in the realization of personalized medicine based on omics (genomic, transcriptomic, or proteomic) data, as well as on images and text.

This Special Issue aims to provide an overview of new and current bioinformatics tools for cancer prediction and prognosis. Contributions may describe novel approaches, or the application of new and existing ones, that aid in the identification of diagnostic, prognostic, or predictive cancer biomarkers; that identify potential therapeutic targets and important cancer-related pathways; or that otherwise provide valuable insight into cancer biology and treatment. To make progress in the field of cancer bioinformatics, contributions by experts in the field in the form of research papers and critical reviews are welcomed.

Dr. Garrett M. Dancik
Dr. Spiros Vlahopoulos
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. Genes 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 2600 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

cancer bioinformatics
biomarkers
biostatistics
genomic sequencing
image recognition
databases

Published Papers (5 papers)

Download All Papers

Research

Open AccessArticle

Radiogenomic Features of GIMAP Family Genes in Clear Cell Renal Cell Carcinoma: An Observational Study on CT Images

and

Genes 2023, 14(10), 1832; https://doi.org/10.3390/genes14101832 (registering DOI) - 22 Sep 2023

Abstract

GTPases of immunity-associated proteins (GIMAP) genes include seven functional genes and a pseudogene. Most of the GIMAPs have a role in the maintenance and development of lymphocytes. GIMAPs could inhibit the development of tumors by increasing the amount and antitumor activity of infiltrating immunocytes. Knowledge of key factors that affect the tumor immune microenvironment for predicting the efficacy of immunotherapy and establishing new targets in ccRCC is of great importance. A computed tomography (CT)-based radiogenomic approach was used to detect the imaging phenotypic features of GIMAP family gene expression in ccRCC. In this retrospective study we enrolled 193 ccRCC patients divided into two groups: ccRCC patients with GIMAP expression (n = 52) and ccRCC patients without GIMAP expression (n = 141). Several imaging features were evaluated on preoperative CT scan. A statistically significant correlation was found with absence of endophytic growth pattern (p = 0.049), tumor infiltration (p = 0.005), advanced age (p = 0.018), and high Fuhrman grade (p = 0.024). This study demonstrates CT imaging features of GIMAP expression in ccRCC. These results could allow the collection of data on GIMAP expression through a CT-approach and could be used for the development of a targeted therapy. Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology for Cancer Prediction and Prognosis)

► Show Figures

Figure 1

Open AccessArticle

Aldehyde Dehydrogenase Genes as Prospective Actionable Targets in Acute Myeloid Leukemia

and

Genes 2023, 14(9), 1807; https://doi.org/10.3390/genes14091807 - 16 Sep 2023

Viewed by 264

Abstract

It has been previously shown that the aldehyde dehydrogenase (ALDH) family member ALDH1A1 has a significant association with acute myeloid leukemia (AML) patient risk group classification and that AML cells lacking ALDH1A1 expression can be readily killed via chemotherapy. In the past, however, a redundancy between the activities of subgroup members of the ALDH family has hampered the search for conclusive evidence to address the role of specific ALDH genes. Here, we describe the bioinformatics evaluation of all nineteen member genes of the ALDH family as prospective actionable targets for the development of methods aimed to improve AML treatment. We implicate ALDH1A1 in the development of recurrent AML, and we show that from the nineteen members of the ALDH family, ALDH1A1 and ALDH2 have the strongest association with AML patient risk group classification. Furthermore, we discover that the sum of the expression values for RNA from the genes, ALDH1A1 and ALDH2, has a stronger association with AML patient risk group classification and survival than either one gene alone does. In conclusion, we identify ALDH1A1 and ALDH2 as prospective actionable targets for the treatment of AML in high-risk patients. Substances that inhibit both enzymatic activities constitute potentially effective pharmaceutics. Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology for Cancer Prediction and Prognosis)

► Show Figures

Figure 1

Open AccessArticle

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning

Christopher J. Pinard

Alex Shenfield

Ali Sadeghi-Naini

and

William T. Tran

Genes 2023, 14(9), 1768; https://doi.org/10.3390/genes14091768 - 07 Sep 2023

Viewed by 336

Abstract

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices. Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology for Cancer Prediction and Prognosis)

► Show Figures

Figure 1

Open AccessArticle

An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma

George K. Matsopoulos

and

Dimitrios D. Koutsouris

Genes 2023, 14(9), 1742; https://doi.org/10.3390/genes14091742 - 31 Aug 2023

Viewed by 346

Abstract

Pancreatic ductal adenocarcinoma (PDAC) constitutes a leading cause of cancer-related mortality despite advances in detection and treatment methods. While computed tomography (CT) serves as the current gold standard for initial evaluation of PDAC, its prognostic value remains limited, as it relies on diagnostic stage parameters encompassing tumor size, lymph node involvement, and metastasis. Radiomics have recently shown promise in predicting postoperative survival of PDAC patients; however, they rely on manual pancreas and tumor delineation by clinicians. In this study, we collected a dataset of pre-operative CT scans from a cohort of 40 PDAC patients to evaluate a fully automated pipeline for survival prediction. Employing nnU-Net trained on an external dataset, we generated automated pancreas and tumor segmentations. Subsequently, we extracted 854 radiomic features from each segmentation, which we narrowed down to 29 via feature selection. We then combined these features with the Tumor, Node, Metastasis (TNM) system staging parameters, as well as the patient’s age. We trained a random survival forest model to perform an overall survival prediction over time, as well as a random forest classifier for the binary classification of two-year survival, using repeated cross-validation for evaluation. Our results exhibited promise, with a mean C-index of 0.731 for survival modeling and a mean accuracy of 0.76 in two-year survival prediction, providing evidence of the feasibility and potential efficacy of a fully automated pipeline for PDAC prognostication. By eliminating the labor-intensive manual segmentation process, our streamlined pipeline demonstrates an efficient and accurate prognostication process, laying the foundation for future research endeavors. Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology for Cancer Prediction and Prognosis)

► Show Figures

Figure 1

Open AccessArticle

Deciphering the Tumor–Immune–Microbe Interactions in HPV-Negative Head and Neck Cancer

Min Hu

Samuel Coleman

Muhammad Zaki Hidayatullah Fadlullah

Daniel Spakowicz

Christine H. Chung

and

Aik Choon Tan

Genes 2023, 14(8), 1599; https://doi.org/10.3390/genes14081599 - 08 Aug 2023

Viewed by 532

Abstract

Patients with human papillomavirus-negative head and neck squamous cell carcinoma (HPV-negative HNSCC) have worse outcomes than HPV-positive HNSCC. In our study, we used a published dataset and investigated the microbes enriched in molecularly classified tumor groups. We showed that microbial signatures could distinguish Hypoxia/Immune phenotypes similar to the gene expression signatures. Furthermore, we identified three highly-correlated microbes with immune processes that are crucial for immunotherapy response. The survival of patients in a molecularly heterogenous group shows significant differences based on the co-abundance of the three microbes. Overall, we present evidence that tumor-associated microbiota are critical components of the tumor ecosystem that may impact tumor microenvironment and immunotherapy response. The results of our study warrant future investigation to experimentally validate the conclusions, which have significant impacts on clinical decision-making, such as treatment selection. Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology for Cancer Prediction and Prognosis)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Special Issue "Bioinformatics and Computational Biology for Cancer Prediction and Prognosis"

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI