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Cancers
Special Issues
Biomedical Informatics and Cancer
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Biomedical Informatics and Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 4980

Special Issue Editor

Prof. Dr. Lang Li

E-Mail Website
Guest Editor
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
Interests: medicine biochemistry; genetics and molecular biology pharmacology; toxicology and pharmaceutics computer science mathematics agricultural and biological sciences engineering neuroscience immunology and microbiology

Special Issue Information

Dear Colleagues,

Biomedical informatics can be broadly applied to cancer research. Fundamentally, it is a form of data science. Most of its applications are driven data, such as multi-omics profiling data from various tissue samples from cancer patients, animal models, and cell cultures, clinical data collected from clinical trials or electronic medical records, or databases developed using various public domain data sources. This Special Issue will focus on cutting-edge biomedical informatics methods used to solve cancer research questions. Biomedical-informatics-led discoveries will be computationally or biologically validated. They include, but are not limited to, the following applications.

  • Target and biomarker discovery and validation using multi-omics data.
  • Tumor heterogeneity and molecular and cellular classification using single cell multi-omics data.
  • Cancer therapy efficacy and adverse event prediction using real world data and evidence.
  • Drug repurposing.
  • Systems biology research on the functions of oncogene, tumor suppressor, essential genes and their crosstalk for tumor progression and metastasis, drug resistance. 

Prof. Dr. Lang Li
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 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. Cancers is an international peer-reviewed open access semimonthly 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 2900 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

  • biomedical informatics
  • multi-omics
  • single cell
  • real world data
  • systems biology
  • drug repurposing

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

Published Papers (3 papers)

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Research

15 pages, 2283 KiB  
Article
Characterizing the Relationship between Expression Quantitative Trait Loci (eQTLs), DNA Methylation Quantitative Trait Loci (mQTLs), and Breast Cancer Risk Variants
by Peh Joo Ho, Alexis Khng, Benita Kiat-Tee Tan, Chiea Chuen Khor, Ern Yu Tan, Geok Hoon Lim, Jian-Min Yuan, Su-Ming Tan, Xuling Chang, Veronique Kiak Mien Tan, Xueling Sim, Rajkumar Dorajoo, Woon-Puay Koh, Mikael Hartman and Jingmei Li
Cancers 2024, 16(11), 2072; https://doi.org/10.3390/cancers16112072 - 30 May 2024
Viewed by 1213
Abstract
Purpose: To assess the association of a polygenic risk score (PRS) for functional genetic variants with the risk of developing breast cancer. Methods: Summary data-based Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) were used to identify breast cancer risk variants associated [...] Read more.
Purpose: To assess the association of a polygenic risk score (PRS) for functional genetic variants with the risk of developing breast cancer. Methods: Summary data-based Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) were used to identify breast cancer risk variants associated with gene expression and DNA methylation levels. A new SMR-based PRS was computed from the identified variants (functional PRS) and compared to an established 313-variant breast cancer PRS (GWAS PRS). The two scores were evaluated in 3560 breast cancer cases and 3383 non-cancer controls and also in a prospective study (n = 10,213) comprising 418 cases. Results: We identified 149 variants showing pleiotropic association with breast cancer risk (eQTLHEIDI > 0.05 = 9, mQTLHEIDI > 0.05 = 165). The discriminatory ability of the functional PRS (AUCcontinuous [95% CI]: 0.540 [0.526 to 0.553]) was found to be lower than that of the GWAS PRS (AUCcontinuous [95% CI]: 0.609 [0.596 to 0.622]). Even when utilizing 457 distinct variants from both the functional and GWAS PRS, the combined discriminatory performance remained below that of the GWAS PRS (AUCcontinuous, combined [95% CI]: 0.561 [0.548 to 0.575]). A binary high/low-risk classification based on the 80th centile PRS in controls revealed a 6% increase in cases using the GWAS PRS compared to the functional PRS. The functional PRS identified an additional 12% of high-risk cases but also led to a 13% increase in high-risk classification among controls. Similar findings were observed in the SCHS prospective cohort, where the GWAS PRS outperformed the functional PRS, and the highest-performing PRS, a combined model, did not significantly improve over the GWAS PRS. Conclusions: While this study identified potentially functional variants associated with breast cancer risk, their inclusion did not substantially enhance the predictive accuracy of the GWAS PRS. Full article
(This article belongs to the Special Issue Biomedical Informatics and Cancer)
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17 pages, 5850 KiB  
Article
L3MBTL3 Is a Potential Prognostic Biomarker and Correlates with Immune Infiltrations in Gastric Cancer
by Lin Gan, Changjiang Yang, Long Zhao, Shan Wang, Yingjiang Ye and Zhidong Gao
Cancers 2024, 16(1), 128; https://doi.org/10.3390/cancers16010128 - 27 Dec 2023
Cited by 1 | Viewed by 1324
Abstract
Recent research has linked lethal (3) malignant brain tumor-like 3 (L3MBTL3) to cancer aggressiveness and a dismal prognosis, but its function in gastric cancer (GC) is unclear. This research investigated the association between L3MBTL3 expression and clinicopathological characteristics of GC cases, as well [...] Read more.
Recent research has linked lethal (3) malignant brain tumor-like 3 (L3MBTL3) to cancer aggressiveness and a dismal prognosis, but its function in gastric cancer (GC) is unclear. This research investigated the association between L3MBTL3 expression and clinicopathological characteristics of GC cases, as well as its prognostic value and biological function based on large-scale databases and clinical samples. The results showed that L3MBTL3 expression was upregulated in malignant GC tissues, which was associated with a shortened survival time and poor clinicopathological characteristics, including TNM staging. A functional enrichment analysis including GO/KEGG and GSEA illustrated the enrichment of different L3MBTL3-associated pathways involved in carcinogenesis and immune response. In addition, the correlations between L3MBTL3 and tumor-infiltrating immune cells were determined based on the TIMER database; the results showed that L3MBTL3 was associated with the immune infiltration of macrophages and their polarization from M1 to M2. Furthermore, our findings suggested a possible function for L3MBTL3 in the regulation of the tumor immune microenvironment of GC. In summary, L3MBTL3 has diagnostic potential, and it also offers new insights into the development of aggressiveness and prognosis in GC. Full article
(This article belongs to the Special Issue Biomedical Informatics and Cancer)
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16 pages, 3321 KiB  
Article
Osteosarcoma Multi-Omics Landscape and Subtypes
by Shan Tang, Ryan D. Roberts, Lijun Cheng and Lang Li
Cancers 2023, 15(20), 4970; https://doi.org/10.3390/cancers15204970 - 13 Oct 2023
Cited by 2 | Viewed by 1798
Abstract
Osteosarcoma (OS) is the most common primary bone malignancy that exhibits remarkable histologic diversity and genetic heterogeneity. The complex nature of osteosarcoma has confounded precise molecular categorization, prognosis, and prediction for this disease. In this study, we performed a comprehensive multiplatform analysis on [...] Read more.
Osteosarcoma (OS) is the most common primary bone malignancy that exhibits remarkable histologic diversity and genetic heterogeneity. The complex nature of osteosarcoma has confounded precise molecular categorization, prognosis, and prediction for this disease. In this study, we performed a comprehensive multiplatform analysis on 86 osteosarcoma tumors, including somatic copy-number alteration, gene expression and methylation, and identified three molecularly distinct and clinically relevant subtypes of osteosarcoma. The subgrouping criteria was validated on another cohort of osteosarcoma tumors. Previously unappreciated osteosarcoma-type-specific changes in specific genes’ copy number, expression and methylation were revealed based on the subgrouping. The subgrouping and novel gene signatures provide insights into refining osteosarcoma therapy and relationships to other types of cancer. Full article
(This article belongs to the Special Issue Biomedical Informatics and Cancer)
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