Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
3.8
Journals
Bioengineering
Special Issues
Recent Advances in Cancer Bioinformatics
share announcement

Recent Advances in Cancer Bioinformatics

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1161

Special Issue Editor

Dr. Pritam Kumar Panda

E-Mail Website
Guest Editor
Omics IT and Data Management Core Facility, German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany
Interests: biophysics; next-generation sequencing analysis; bioinformatics

Special Issue Information

Dear Colleagues,

This Special Issue on "Recent Advances in Cancer Bioinformatics" aims to present the latest research and developments in the field of bioinformatics and its applications in cancer research. This Special Issue will focus on innovative computational techniques and their applications in understanding cancer biology, diagnosing various cancer types, and developing targeted therapies. We welcome original research articles, reviews, and case studies that highlight significant contributions to the following areas:

  1. Modeling in Cancer Bioinformatics:
  • Computational models to simulate cancer progression and treatment response.
  • Network-based approaches to understand cancer pathways and interactions.
  • Predictive modeling for personalized medicine.
  1. Biophysics of Cancer:
  • Physical principles underlying cancer cell behavior.
  • Mechanistic insights into tumor growth and metastasis.
  • Biophysical techniques for cancer diagnosis and therapy.
  1. Single-Cell Sequencing:
  • Techniques and applications in single-cell RNA sequencing (scRNA-seq).
  • Insights into tumor heterogeneity and microenvironment.
  • Single-cell data integration and interpretation.
  1. Whole Exome Sequencing (WES) and Whole Genome Sequencing (WGS):
  • Advances in WES/WGS technologies and their application in cancer genomics.
  • Identification of novel cancer biomarkers and mutations.
  • Comparative analysis of WES/WGS data in different cancer types.
  1. RNA Sequencing (RNA-seq):
  • High-throughput RNA-seq techniques for cancer transcriptomics.
  • Differential expression analysis and its implications for cancer.
  • RNA-seq data integration with other omics data for comprehensive cancer profiling.

This Special Issue seeks to bring together pioneering studies that leverage bioinformatics to decode the complexities of cancer, offering insights into mechanisms of oncogenesis, tumor evolution, and resistance to therapies. We aim to provide a platform for interdisciplinary collaboration and the exchange of cutting-edge ideas in cancer research.

Dr. Pritam Kumar Panda
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. Bioengineering 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

  • cancer bioinformatics
  • computational modeling
  • cancer biophysics
  • single-cell sequencing
  • whole exome sequencing (WES)
  • whole genome sequencing (WGS)
  • RNA sequencing (RNA-seq)
  • tumor heterogeneity
  • cancer genomics
  • personalized medicine
  • predictive modeling
  • cancer pathways
  • oncogenesis
  • tumor microenvironment
  • bioinformatics tools
  • omics integration

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 16427 KiB  
Article
Leveraging Diverse Cell-Death Patterns to Decipher the Interactive Relation of Unfavorable Outcome and Tumor Microenvironment in Breast Cancer
by Yue Li, Ting Ding, Tong Zhang, Shuangyu Liu, Jinhua Wang, Xiaoyan Zhou, Zeqi Guo, Qian He and Shuqun Zhang
Bioengineering 2025, 12(4), 420; https://doi.org/10.3390/bioengineering12040420 - 15 Apr 2025
Viewed by 255
Abstract
Background: Programmed cell death (PCD) dynamically influences breast cancer (BC) prognosis through interactions with the tumor microenvironment (TME). We investigated 13 PCD patterns to decipher their prognostic impact and mechanistic links to TME-driven outcomes. Our study aimed to explore the complex mechanisms underlying [...] Read more.
Background: Programmed cell death (PCD) dynamically influences breast cancer (BC) prognosis through interactions with the tumor microenvironment (TME). We investigated 13 PCD patterns to decipher their prognostic impact and mechanistic links to TME-driven outcomes. Our study aimed to explore the complex mechanisms underlying these interactions and establish a prognostic prediction model for breast cancer. Methods: Using TCGA and METABRIC datasets, we integrated single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network analysis (WGCNA), and Least Absolute Shrinkage and Selection Operator (LASSO) to explore PCD-TME interactions. Multi-dimensional analyses included immune infiltration, genomic heterogeneity, and functional pathway enrichment. Results: Our results indicated that high apoptosis and pyroptosis activity, along with low autophagy, correlated with favorable prognosis, which was driven by enhanced anti-tumor immunity, including more M1 macrophage polarization and activated CD8+ T cells in TME. PCD-related genes could promote tumor metastasis and poor prognosis via VEGF/HIF-1/MAPK signaling and immune response, including Th1/Th2 cell differentiation, while new tumor event occurrences (metastasis/secondary cancers) were linked to specific clinical features and gene mutation spectrums, including TP53/CDH1 mutations and genomic instability. We constructed a six-gene LASSO model (BCAP31, BMF, GLUL, NFKBIA, PARP3, PROM2) to predict prognosis and identify high-risk BC patients (for five-year survival, AUC = 0.76 in TCGA; 0.74 in METABRIC). Therein, the high-risk subtype patients demonstrated a poorer prognosis, also characterized by lower microenvironment matrix and downregulated immunocyte infiltration. These six gene signatures also showed prognostic value with significant differential expression in gene and protein levels of BC samples. Conclusion: Our study provided a comprehensive landscape of the cancer survival difference and related PCD-TME interaction axis and highlighted that high-apoptosis/pyroptosis states caused favorable prognosis, underlying mechanisms closely related with the TME where anti-tumor immunity would be beneficial for patient prognosis. These findings highlighted the model’s potential for risk stratification in BC. Full article
(This article belongs to the Special Issue Recent Advances in Cancer Bioinformatics)
►▼ Show Figures

Figure 1

19 pages, 11436 KiB  
Article
Molecular Signatures of Cancer Stemness Characterize the Correlations with Prognosis and Immune Landscape and Predict Risk Stratification in Pheochromocytomas and Paragangliomas
by Lei Li, Shuangyu Liu, Zeqi Guo, Yueming Tang, Yue Zhang, Ling Qiu and Yue Li
Bioengineering 2025, 12(3), 219; https://doi.org/10.3390/bioengineering12030219 - 21 Feb 2025
Viewed by 619
Abstract
Background: Pheochromocytoma and paragangliomas (PPGLs) caused refractory hypertension in clinics. The sustained risk of local or metastatic recurrences or new tumor development prompted more research on diagnosis, prognosis prediction, and immunotherapy. Method: The tumor stemness is closely related to the heterogeneous growth of [...] Read more.
Background: Pheochromocytoma and paragangliomas (PPGLs) caused refractory hypertension in clinics. The sustained risk of local or metastatic recurrences or new tumor development prompted more research on diagnosis, prognosis prediction, and immunotherapy. Method: The tumor stemness is closely related to the heterogeneous growth of tumor, metastasis, and drug-resistance, and mRNA expression-based stemness indices (mRNAsi) could reflect tumor stemness. This was calculated based on OCLR machine learning algorithm and PPGLs patients’ TCGA RNAseq data. The relationship between clinical, molecular, and tumor microenvironment (TME) features and tumor stemness was analyzed through the hub genes that best captured the stem cell characteristics of PPGLs using weighted gene co-expression network analysis (WGCNA), Cox, and LASSO regression analysis. Results: Our study found that metastatic PPGLs had higher mRNAsi scores, suggesting the degree of tumor stemness could affect metastasis and progression. HRAS, CSDE1, NF1, RET, and VHL-mutant subtypes displayed significant difference in stemness expression. Patients were divided into stemness high-score and low-score subtypes. High-score PPGLs displayed the more unfavorable prognosis compared with low-score, associated with their immune-suppressive features, manifested as low macrophages M1 infiltration and downregulated expression of immune checkpoints. Furthermore, from the viewpoint of stemness features, we established a reliable prognostic for PPGLs, which has the highest AUC value (0.908) in the field so far. And this could stratify PPGLs patients into high-risk and low-risk subtypes, showing the significant differences in prognosis, underlying mechanisms correlated with specific molecular alterations, biological processes activation, and TME. Notably, high immune infiltration and tumor neoantigen in low-risk patients and further resulted in more responsive to immunotherapy. Conclusion: We indicated that tumor stemness could act as the potential biomarker for metastasis or prognosis of PPGLs, and integrated multi-data sources, analyzed valuable stemness-related genes, developed and verified a novel stemness scoring system to predict prognosis and guide the choice of treatment strategies. Full article
(This article belongs to the Special Issue Recent Advances in Cancer Bioinformatics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop