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Biomedicines
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Advanced Research in Cancer Genomics and Genetics: Evolution from Single-Gene...
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Advanced Research in Cancer Genomics and Genetics: Evolution from Single-Gene Analysis to AI-Driven Data Analysis of High-Throughput Data Generation

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular Genetics and Genetic Diseases".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 4188

Special Issue Editor

Dr. Roberto Piergentili

E-Mail Website
Guest Editor
Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
Interests: chromatin structure and function; mitosis; meiosis; urogenital cancer; cancer epigenetics; cytokinesis; DNA repair; telomeric DNA; heterochromatin

Special Issue Information

Dear Colleagues,

The advancement in omics disciplines, especially those directly involved in gene function and expression (e.g., genomics, epigenomics, exomics, transcriptomics, proteomics), as well as those describing the genome response to drugs in the treatment of various disorders (such as pharmacogenomics and metabolomics) allow us to have a comprehensive overview of gene activity inside cells in health and disease. The increasing power of technology in data retrieval allows us to collect huge amounts of information that need to be elaborated, to create models, and to set the basis for personalized medicine. In this context, research on neoplastic transformation benefits significantly from these approaches, yet a comprehensive view of how cancer forms and develops over time is still far from possible. Cancer is a multistep and complex disease; its formation is influenced by several factors including—but not limited to—the genetic background of patients, their habits, the environment in which they live, and above all, the multiple interactions among these different layers of inputs. An integrated approach is necessary, involving not only high-throughput data generation, but also its contextualization. In this scenario, the use of AI is extremely promising for a more accurate diagnosis and prognosis and for the planning of tailored treatment strategies.

In this Special Issue, we welcome reviews, original research articles, and short communications that focus on, or are relevant to, the use of genetics and genomics approaches in cancer, the response of genes to environmental insults, and their up- or down-regulation upon drug administration, as well as new approaches for the early diagnosis and custom treatment of cancer.

Dr. Roberto Piergentili
Guest Editor

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. Biomedicines 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
  • genetics
  • epigenetics
  • genomics
  • epigenomics
  • gene-environment interaction
  • cancer diagnosis and prognosis
  • personalized medicine
  • artificial intelligence and cancer

Published Papers (2 papers)

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Research

12 pages, 1951 KiB  
Article
Radiomics-Clinical AI Model with Probability Weighted Strategy for Prognosis Prediction in Non-Small Cell Lung Cancer
by Fuk-Hay Tang, Yee-Wai Fong, Shing-Hei Yung, Chi-Kan Wong, Chak-Lap Tu and Ming-To Chan
Biomedicines 2023, 11(8), 2093; https://doi.org/10.3390/biomedicines11082093 - 25 Jul 2023
Cited by 1 | Viewed by 1435
Abstract
In this study, we propose a radiomics clinical probability-weighted model for the prediction of prognosis for non-small cell lung cancer (NSCLC). The model combines radiomics features extracted from radiotherapy (RT) planning images with clinical factors such as age, gender, histology, and tumor stage. [...] Read more.
In this study, we propose a radiomics clinical probability-weighted model for the prediction of prognosis for non-small cell lung cancer (NSCLC). The model combines radiomics features extracted from radiotherapy (RT) planning images with clinical factors such as age, gender, histology, and tumor stage. CT images with radiotherapy structures of 422 NSCLC patients were retrieved from The Cancer Imaging Archive (TCIA). Radiomic features were extracted from gross tumor volumes (GTVs). Five machine learning algorithms, namely decision trees (DT), random forests (RF), extreme boost (EB), support vector machine (SVM) and generalized linear model (GLM) were optimized by a voted ensemble machine learning (VEML) model. A probabilistic weighted approach is used to incorporate the uncertainty associated with both radiomic and clinical features and to generate a probabilistic risk score for each patient. The performance of the model is evaluated using a receiver operating characteristic (ROC). The Radiomic model, clinical factor model, and combined radiomic clinical probability-weighted model demonstrated good performance in predicting NSCLC survival with AUC of 0.941, 0.856 and 0.949, respectively. The combined radiomics clinical probability-weighted enhanced model achieved significantly better performance than the radiomic model in 1-year survival prediction (chi-square test, p < 0.05). The proposed model has the potential to improve NSCLC prognosis and facilitate personalized treatment decisions. Full article
(This article belongs to the Special Issue Advanced Research in Cancer Genomics and Genetics: Evolution from Single-Gene Analysis to AI-Driven Data Analysis of High-Throughput Data Generation)
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16 pages, 1935 KiB  
Article
Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring
by Chih-Tung Yeh, Gen-Yih Liao and Takeshi Emura
Biomedicines 2023, 11(3), 797; https://doi.org/10.3390/biomedicines11030797 - 6 Mar 2023
Cited by 14 | Viewed by 2284
Abstract
Prognostic analysis for patient survival often employs gene expressions obtained from high-throughput screening for tumor tissues from patients. When dealing with survival data, a dependent censoring phenomenon arises, and thus the traditional Cox model may not correctly identify the effect of each gene. [...] Read more.
Prognostic analysis for patient survival often employs gene expressions obtained from high-throughput screening for tumor tissues from patients. When dealing with survival data, a dependent censoring phenomenon arises, and thus the traditional Cox model may not correctly identify the effect of each gene. A copula-based gene selection model can effectively adjust for dependent censoring, yielding a multi-gene predictor for survival prognosis. However, methods to assess the impact of various types of dependent censoring on the multi-gene predictor have not been developed. In this article, we propose a sensitivity analysis method using the copula-graphic estimator under dependent censoring, and implement relevant methods in the R package “compound.Cox”. The purpose of the proposed method is to investigate the sensitivity of the multi-gene predictor to a variety of dependent censoring mechanisms. In order to make the proposed sensitivity analysis practical, we develop a web application. We apply the proposed method and the web application to a lung cancer dataset. We provide a template file so that developers can modify the template to establish their own web applications. Full article
(This article belongs to the Special Issue Advanced Research in Cancer Genomics and Genetics: Evolution from Single-Gene Analysis to AI-Driven Data Analysis of High-Throughput Data Generation)
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