Journal of Oncology

Bioinformatics in Cancer and Immune Microenvironment


Publishing date
01 May 2022
Status
Open
Submission deadline
17 Dec 2021

Lead Editor
Guest Editors

1Neonatal Department, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China

2Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China

3Department Pain Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China

4University of Verona Medical School, Verona, China


Bioinformatics in Cancer and Immune Microenvironment


Call for papers

This Issue is now open for submissions.

Papers are published upon acceptance, regardless of the Special Issue publication date.

 Submit to this Special Issue

Description

Tumor cells do not exist in isolation in the microenvironment of malignancy but rather in a diverse ecosystem that includes not only heterogeneous tumor cell clones, but also normal cell types like fibroblasts, vasculature, and a large pool of immune cells at various stages of activation and differentiation. As a consequence, there is now a complicated interaction of different cellular signaling pathways with the immune cell component influencing cancer development and therapeutic response. To capitalize on potential therapeutic and biomarker findings, it is challenging and time-consuming to thoroughly and systematically characterize these different cell types from diverse tumor samples using immunohistochemistry. We have now entered an era of digital cancer treatment, fueled by the increasing availability of omics technologies, in which varied and global molecular profiling may drive therapeutic discovery and predict immunotherapy responses. Computationally extracting cell-type specific information directly from bulk tumors is one promising answer to this problem. The benefit of using bioinformatics to predict accurate immune signatures from bulk native tumor tissue is that the functional intracellular and intercellular transcriptome profiles are preserved, whereas when purified immune cells are isolated from native tissue, the molecular profiles will have inherently different patterns. Such in silico methods are beneficial because they may capture cell-type specific characteristics as well as cell-cell interactions at the tissue system level.

Predicting tumor patterns accurately and completely is a significant problem to solve, especially considering the effectiveness of immunotherapeutic medication therapy for many human malignancies. This is particularly difficult for subgroups of closely similar immune cell phenotypes with modest gene expression variations but significant functional differences. It will be very beneficial to use bioinformatics strategies to profile the tumor immune landscape because it will allow us to systematically profile one immune cell population's patterns and immune cell networks in tumors with higher resolution, which will aid in the discovery of immune-modulatory drugs.

The goal of this Special Issue is to bring together original research and review articles that will help researchers better understand the complex interactions that exist between tumors, immune cells, and the microenvironment, as well as the discovery of new biomarkers and molecular targets in various cancers.

Potential topics include but are not limited to the following:

  • Bioinformatics research into new cancer diagnosis and treatment methods based on the immune microenvironment
  • Immunological, gene regulatory, and pathophysiological mechanisms involved in carcinogenesis
  • The crosstalk between immune and nonimmune cells in the tumor microenvironment
  • Drug target discovery and targeted drug design
  • Therapeutic strategies that modulate the immune response
  • Meta-analysis to identify the function of tumor microenvironment in cancer

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 4758364
  • - Review Article

The Potential Therapeutic Role of Mesenchymal Stem Cells-Derived Exosomes in Osteoradionecrosis

Yuetian Li | Xinyue Wang | ... | Bo Huang
  • Special Issue
  • - Volume 2021
  • - Article ID 9219961
  • - Research Article

Development of a Prognostic Model Based on the Identification of EMT-Related lncRNAs in Triple-Negative Breast Cancer

Jiani Guo | Xuesong Yi | ... | Mingde Huang
  • Special Issue
  • - Volume 2021
  • - Article ID 1331031
  • - Research Article

Construction of a Macrophage Infiltration Regulatory Network and Related Prognostic Model of High-Grade Serous Ovarian Cancer

Hua Chang | Yuyan Zhu | ... | Jihang Yao
  • Special Issue
  • - Volume 2021
  • - Article ID 2719172
  • - Research Article

Construction and Validation of a Potent Epigenetic Modification-Related Prognostic Signature for Osteosarcoma Patients

Siyu Liu | Bing Wu | ... | Songtao Ai
  • Special Issue
  • - Volume 2021
  • - Article ID 1334571
  • - Research Article

Identification of an Immune-Related Biomarker Model Based on the CircRNA-Associated Regulatory Network for Esophageal Carcinoma

Zhaonian Hu | Jun Xie | ... | Song Han
  • Special Issue
  • - Volume 2021
  • - Article ID 4701680
  • - Research Article

EFNB1 Acts as a Novel Prognosis Marker in Glioblastoma through Bioinformatics Methods and Experimental Validation

Yaohong Shi | Yuanyuan Sun | ... | Chen Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 2182761
  • - Research Article

Prognostic Value of a Pyroptosis-Related Long Noncoding RNA Signature Associated with Osteosarcoma Microenvironment

Xinxin Bu | Jiuxiang Liu | ... | Zhi Li
  • Special Issue
  • - Volume 2021
  • - Article ID 6718443
  • - Research Article

Necroptosis-Related lncRNAs: Predicting Prognosis and the Distinction between the Cold and Hot Tumors in Gastric Cancer

Zirui Zhao | Haohan Liu | ... | Jianbo Xu
  • Special Issue
  • - Volume 2021
  • - Article ID 3658196
  • - Review Article

Ferroptosis-Associated Classifier and Indicator for Prognostic Prediction in Cutaneous Melanoma

Hao Zeng | Cong You | ... | Longying Deng
  • Special Issue
  • - Volume 2021
  • - Article ID 9987376
  • - Research Article

m6A-Mediated Tumor Invasion and Methylation Modification in Breast Cancer Microenvironment

Fei Liu | Xiaopeng Yu | Guijin He
Journal of Oncology
 Journal metrics
Acceptance rate24%
Submission to final decision66 days
Acceptance to publication37 days
CiteScore3.100
Journal Citation Indicator0.680
Impact Factor4.375

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.