Computational Biology and Biostatistics for Public Health

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 5299

Special Issue Editor


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Guest Editor
Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China
Interests: computational biology; statistical inference; systems epidemiology

Special Issue Information

Dear Colleagues,

The Special Issue will showcase basic and applied research dealing with statistical issues of relevance to environmental health, including the development of new methodologies and application of existing techniques in novel ways to address public health problems.

Research in the Special Issue focuses on several areas:

  1. Design and analysis of laboratory animal toxicology/carcinogenicity experiments and development of improved statistical methods in human health research;
  2. Application and development of methodologies for epidemiological and clinical human studies;
  3. Application and development of new bioinformatics techniques for harvesting information from high-dimensional genomic, gene expression and proteomic data;
  4. Development of new design and analysis approaches in statistical genetics;
  5. Development of broadly applicable statistical approaches.

Prof. Dr. Mingqing Xu
Guest Editor

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Keywords

  • computational biology
  • statistical modeling
  • systems epidemiology

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Published Papers (2 papers)

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Research

14 pages, 32468 KiB  
Article
Anoikis-Related Long Non-Coding RNA Signatures to Predict Prognosis and Immune Infiltration of Gastric Cancer
by Wen-Jun Meng, Jia-Min Guo, Li Huang, Yao-Yu Zhang, Yue-Ting Zhu, Lian-Sha Tang, Jia-Ling Wang, Hong-Shuai Li and Ji-Yan Liu
Bioengineering 2024, 11(9), 893; https://doi.org/10.3390/bioengineering11090893 - 5 Sep 2024
Cited by 9 | Viewed by 2339
Abstract
Anoikis is a distinct type of programmed cell death and a unique mechanism for tumor progress. However, its exact function in gastric cancer (GC) remains unknown. This study aims to investigate the function of anoikis-related lncRNA (ar-lncRNA) in the prognosis of GC and [...] Read more.
Anoikis is a distinct type of programmed cell death and a unique mechanism for tumor progress. However, its exact function in gastric cancer (GC) remains unknown. This study aims to investigate the function of anoikis-related lncRNA (ar-lncRNA) in the prognosis of GC and its immunological infiltration. The ar-lncRNAs were derived from RNA sequencing data and associated clinical information obtained from The Cancer Genome Atlas. Pearson correlation analysis, differential screening, LASSO and Cox regression were utilized to identify the typical ar-lncRNAs with prognostic significance, and the corresponding risk model was constructed, respectively. Comprehensive methods were employed to assess the clinical characteristics of the prediction model, ensuring the accuracy of the prediction results. Further analysis was conducted on the relationship between immune microenvironment and risk features, and sensitivity predictions were made about anticancer medicines. A risk model was built according to seven selected ar-lncRNAs. The model was validated and the calibration plots were highly consistent in validating nomogram predictions. Further analyses revealed the great accuracy of the model and its ability to serve as a stand-alone GC prognostic factor. We subsequently disclosed that high-risk groups display significant enrichment in pathways related to tumors and the immune system. Additionally, in tumor immunoassays, notable variations in immune infiltrates and checkpoints were noted between different risk groups. This study proposes, for the first time, that prognostic signatures of ar-lncRNA can be established in GC. These signatures accurately predict the prognosis of GC and offer potential biomarkers, suggesting new avenues for basic research, prognosis prediction and personalized diagnosis and treatment of GC. Full article
(This article belongs to the Special Issue Computational Biology and Biostatistics for Public Health)
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15 pages, 2269 KiB  
Article
Pretreatment Sarcopenia and MRI-Based Radiomics to Predict the Response of Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer
by Jiamin Guo, Wenjun Meng, Qian Li, Yichen Zheng, Hongkun Yin, Ying Liu, Shuang Zhao and Ji Ma
Bioengineering 2024, 11(7), 663; https://doi.org/10.3390/bioengineering11070663 - 28 Jun 2024
Cited by 5 | Viewed by 2398
Abstract
The association between sarcopenia and the effectiveness of neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) remains uncertain. This study aims to examine the potential of sarcopenia as a predictive factor for the response to NAC in TNBC, and to assess whether its [...] Read more.
The association between sarcopenia and the effectiveness of neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) remains uncertain. This study aims to examine the potential of sarcopenia as a predictive factor for the response to NAC in TNBC, and to assess whether its combination with MRI radiomic signatures can improve the predictive accuracy. We collected clinical and pathological information, as well as pretreatment breast MRI and abdominal CT images, of 121 patients with TNBC who underwent NAC at our hospital between January 2012 and September 2021. The presence of pretreatment sarcopenia was assessed using the L3 skeletal muscle index. Clinical models were constructed based on independent risk factors identified by univariate regression analysis. Radiomics data were extracted on breast MRI images and the radiomics prediction models were constructed. We integrated independent risk factors and radiomic features to build the combined models. The results of this study demonstrated that sarcopenia is an independent predictive factor for NAC efficacy in TNBC. The combination of sarcopenia and MRI radiomic signatures can further improve predictive performance. Full article
(This article belongs to the Special Issue Computational Biology and Biostatistics for Public Health)
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