Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
3.5
Journals
Genes
Special Issues
Application of Bioinformatics in Human Cancers
share announcement

Application of Bioinformatics in Human Cancers

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 16254

Special Issue Editor

Prof. Dr. Shuangge Steven Ma

E-Mail Website
Guest Editor
Department of Biostatistics, Yale University, 60 College Street, New Haven, CT 06520, USA
Interests: cancer biostatistics; genetic epidemiology; high-dimensional data analysis; health economics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to cordially invite you to participate in this Special Issue “Application of Bioinformatics in Human Cancers”.

Cancer is an omics disease. In recent decades, we have witnessed a surge in cancer omics profiling studies. Accordingly, a myriad of novel bioinformatics methods have been developed to analyze such studies so as to better understand cancer biology and guide treatment development. It is recognized that it may be the time to conduct comprehensive reviews, evaluations, and comparisons. Equally important, it is also recognized that, despite tremendous successes, the existing methods still have limitations, and new methodological developments are strongly needed. As such, of interest to this Special Issue are original and review articles that describe novel bioinformatics methods and case studies for various human cancers.

We strongly welcome submissions from cancer bioinformatics researchers with all backgrounds, interests, and specializations.

Dr. Shuangge Ma
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. Genes 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

  • human cancer
  • bioinformatics methodology
  • computational algorithm and software
  • cancer genetic epidemiology
  • integrated bioinformatics analysis
  • methodological comparative study

Published Papers (7 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

46 pages, 2037 KiB  
Article
Survival Analysis with High-Dimensional Omics Data Using a Threshold Gradient Descent Regularization-Based Neural Network Approach
by Yu Fan, Sanguo Zhang and Shuangge Ma
Genes 2022, 13(9), 1674; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13091674 - 19 Sep 2022
Cited by 2 | Viewed by 1677
Abstract
Analysis of data with a censored survival response and high-dimensional omics measurements is now common. Most of the existing analyses are based on specific (semi)parametric models, in particular the Cox model. Such analyses may be limited by not having sufficient flexibility, for example, [...] Read more.
Analysis of data with a censored survival response and high-dimensional omics measurements is now common. Most of the existing analyses are based on specific (semi)parametric models, in particular the Cox model. Such analyses may be limited by not having sufficient flexibility, for example, in accommodating nonlinearity. For categorical and continuous responses, neural networks (NNs) have provided a highly competitive alternative. Comparatively, NNs for censored survival data remain limited. Omics measurements are usually high-dimensional, and only a small subset is expected to be survival-associated. As such, regularized estimation and selection are needed. In the existing NN studies, this is usually achieved via penalization. In this article, we propose adopting the threshold gradient descent regularization (TGDR) technique, which has competitive performance (for example, when compared to penalization) and unique advantages in regression analysis, but has not been adopted with NNs. The TGDR-based NN has a highly sensible formulation and an architecture different from the unregularized and penalization-based ones. Simulations show its satisfactory performance. Its practical effectiveness is further established via the analysis of two cancer omics datasets. Overall, this study can provide a practical and useful new way in the NN paradigm for survival analysis with high-dimensional omics measurements. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Human Cancers)
►▼ Show Figures

Figure 1

16 pages, 3018 KiB  
Article
Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors—Dependencies and Novel Pitfalls
by André Marquardt, Philip Kollmannsberger, Markus Krebs, Antonella Argentiero, Markus Knott, Antonio Giovanni Solimando and Alexander Georg Kerscher
Genes 2022, 13(8), 1335; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13081335 - 26 Jul 2022
Viewed by 1646
Abstract
Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction [...] Read more.
Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Human Cancers)
►▼ Show Figures

Figure 1

13 pages, 3225 KiB  
Article
Profiling the Tumor-Infiltrating Lymphocytes in Gastric Cancer Reveals Its Implication in the Prognosis
by Weiqiang Yu, Shuaili Wang, Qiqi Rong, Olugbenga Emmanuel Ajayi, Kongwang Hu and Qingfa Wu
Genes 2022, 13(6), 1017; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13061017 - 05 Jun 2022
Cited by 2 | Viewed by 2805
Abstract
Gastric cancer is the fifth most common malignancy and the third leading cause of cancer-related mortality worldwide. Immunotherapy offers promising new treatment options for gastric cancer patients; however, it is only effective in a limited fraction of patients. In this study, we evaluated [...] Read more.
Gastric cancer is the fifth most common malignancy and the third leading cause of cancer-related mortality worldwide. Immunotherapy offers promising new treatment options for gastric cancer patients; however, it is only effective in a limited fraction of patients. In this study, we evaluated the composition of 22 tumor-infiltrating lymphocytes (TILs) in TCGA Stomach Adenocarcinoma (STAD) using deconvolution-based method by analyzing the publicly available bulk tumor RNA-seq data. The patients were classified into high-TIL and low-TIL subtypes based on their immune cell profiles and prognosis outputs. The differentially expressed genes (DEGs) between the two subtypes were identified, and GO/KEGG analysis showed that broad immune genes, such as PD-L1 and PD-1, were highly expressed in the high-TIL subtype. A comprehensive protein–protein interaction (PPI) network centered on DEGs was built, and 16 hub genes of the network were further identified. Based on the hub genes, an elastic model with 11 gene signatures (NKG7, GZMB, IL2RB, CCL5, CD8A, IDO1, MYH1, GNLY, CXCL11, GBP5 and PRF1) was developed to predict the high-TIL subtype. In summary, our findings showed that the compositions of TILs within the tumor immune microenvironment of stomach cancer patients are highly heterogeneous, and the profiles of TILs have the potential to be predictive markers of patients’ responses and overall survival outcomes. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Human Cancers)
►▼ Show Figures

Figure 1

15 pages, 4525 KiB  
Article
The Value of the Stemness Index in Ovarian Cancer Prognosis
by Hongjun Yuan, Qian Yu, Jianyu Pang, Yongzhi Chen, Miaomiao Sheng and Wenru Tang
Genes 2022, 13(6), 993; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13060993 - 31 May 2022
Cited by 8 | Viewed by 2223
Abstract
Ovarian cancer (OC) is one of the most common gynecological malignancies. It is associated with a difficult diagnosis and poor prognosis. Our study aimed to analyze tumor stemness to determine the prognosis feature of patients with OC. At this job, we selected the [...] Read more.
Ovarian cancer (OC) is one of the most common gynecological malignancies. It is associated with a difficult diagnosis and poor prognosis. Our study aimed to analyze tumor stemness to determine the prognosis feature of patients with OC. At this job, we selected the gene expression and the clinical profiles of patients with OC in the TCGA database. We calculated the stemness index of each patient using the one-class logistic regression (OCLR) algorithm and performed correlation analysis with immune infiltration. We used consensus clustering methods to classify OC patients into different stemness subtypes and compared the differences in immune infiltration between them. Finally, we established a prognostic signature by Cox and LASSO regression analysis. We found a significant negative correlation between a high stemness index and immune score. Pathway analysis indicated that the differentially expressed genes (DEGs) from the low- and high-mRNAsi groups were enriched in multiple functions and pathways, such as protein digestion and absorption, the PI3K-Akt signaling pathway, and the TGF-β signaling pathway. By consensus cluster analysis, patients with OC were split into two stemness subtypes, with subtype II having a better prognosis and higher immune infiltration. Furthermore, we identified 11 key genes to construct the prognostic signature for patients with OC. Among these genes, the expression levels of nine, including SFRP2, MFAP4, CCDC80, COL16A1, DUSP1, VSTM2L, TGFBI, PXDN, and GAS1, were increased in the high-risk group. The analysis of the KM and ROC curves indicated that this prognostic signature had a great survival prediction ability and could independently predict the prognosis for patients with OC. We established a stemness index-related risk prognostic module for OC, which has prognostic-independent capabilities and is expected to improve the diagnosis and treatment of patients with OC. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Human Cancers)
►▼ Show Figures

Figure 1

27 pages, 19141 KiB  
Article
A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data
by Ziye Luo, Yuzhao Zhang and Yifan Sun
Genes 2022, 13(4), 702; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13040702 - 15 Apr 2022
Viewed by 1430
Abstract
In high-throughput profiling studies, extensive efforts have been devoted to searching for the biomarkers associated with the development and progression of complex diseases. The heterogeneity of covariate effects associated with the outcomes across subjects has been noted in the literature. In this paper, [...] Read more.
In high-throughput profiling studies, extensive efforts have been devoted to searching for the biomarkers associated with the development and progression of complex diseases. The heterogeneity of covariate effects associated with the outcomes across subjects has been noted in the literature. In this paper, we consider a scenario where the effects of covariates change smoothly across subjects, which are ordered by a known auxiliary variable. To this end, we develop a penalization-based approach, which applies a penalization technique to simultaneously select important covariates and estimate their unique effects on the outcome variables of each subject. We demonstrate that, under the appropriate conditions, our method shows selection and estimation consistency. Additional simulations demonstrate its superiority compared to several competing methods. Furthermore, applying the proposed approach to two The Cancer Genome Atlas datasets leads to better prediction performance and higher selection stability. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Human Cancers)
►▼ Show Figures

Figure 1

22 pages, 20783 KiB  
Article
DLK2 Acts as a Potential Prognostic Biomarker for Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
by Man-Gang Lee, Yung-Kuo Lee, Shih-Chung Huang, Chen-Lin Chang, Chou-Yuan Ko, Wen-Chin Lee, Tung-Yuan Chen, Shiow-Jyu Tzou, Cheng-Yi Huang, Ming-Hong Tai, Yu-Wei Lin, Mei-Lang Kung, Ming-Chao Tsai, Yung-Lung Chen, Yi-Chen Chang, Zhi-Hong Wen, Chao-Cheng Huang and Tian-Huei Chu
Genes 2022, 13(4), 629; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13040629 - 01 Apr 2022
Cited by 3 | Viewed by 2741
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most common RCC subtype with a high mortality. It has been reported that delta-like 1 homologue (DLK1) participates in the tumor microenvironmental remodeling of ccRCC, but the relationship between delta-like 2 homologue (DLK2, a DLK1 [...] Read more.
Clear cell renal cell carcinoma (ccRCC) is the most common RCC subtype with a high mortality. It has been reported that delta-like 1 homologue (DLK1) participates in the tumor microenvironmental remodeling of ccRCC, but the relationship between delta-like 2 homologue (DLK2, a DLK1 homologue) and ccRCC is still unclear. Thus, this study aims to investigate the role of DLK2 in the biological function and disease prognosis of ccRCC using bioinformatics analysis. The TNMplot database showed that DLK2 was upregulated in ccRCC tissues. From the UALCAN analysis, the overexpression of DLK2 was associated with advanced stage and high grade in ccRCC. Moreover, the Kaplan-Meier plotter (KM Plotter) database showed that DLK2 upregulation was associated with poor survival outcome in ccRCC. By the LinkedOmics analysis, DLK2 signaling may participated in the modulation of ccRCC extracellular matrix (ECM), cell metabolism, ribosome biogenesis, TGF-β signaling and Notch pathway. Besides, Tumor Immune Estimation Resource (TIMER) analysis showed that the macrophage and CD8+ T cell infiltrations were associated with good prognosis in ccRCC patients. Finally, DLK2 overexpression was associated with the reduced macrophage recruitments and the M1–M2 polarization of macrophage in ccRCC tissues. Together, DLK2 may acts as a novel biomarker, even therapeutic target in ccRCC. However, this study lacks experimental validation, and further studies are required to support this viewpoint. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Human Cancers)
►▼ Show Figures

Figure 1

15 pages, 2054 KiB  
Article
Molecularly Guided Drug Repurposing for Cholangiocarcinoma: An Integrative Bioinformatic Approach
by Simran Venkatraman, Brinda Balasubramanian, Pisut Pongchaikul, Rutaiwan Tohtong and Somchai Chutipongtanate
Genes 2022, 13(2), 271; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13020271 - 29 Jan 2022
Cited by 5 | Viewed by 2883
Abstract
Background: Cholangiocarcinoma (CCA) has a complex immune microenvironment architecture, thus possessing challenges in its characterization and treatment. This study aimed to repurpose FDA-approved drugs for cholangiocarcinoma by transcriptomic-driven bioinformatic approach. Methods: Cox-proportional univariate regression was applied to 3017 immune-related genes known a priori [...] Read more.
Background: Cholangiocarcinoma (CCA) has a complex immune microenvironment architecture, thus possessing challenges in its characterization and treatment. This study aimed to repurpose FDA-approved drugs for cholangiocarcinoma by transcriptomic-driven bioinformatic approach. Methods: Cox-proportional univariate regression was applied to 3017 immune-related genes known a priori to identify a list of mortality-associated genes, so-called immune-oncogenic gene signature, in CCA tumor-derived RNA-seq profiles of two independent cohorts. Unsupervised clustering stratified CCA tumors into two groups according to the immune-oncogenic gene signature expression, which then confirmed its clinical relevance by Kaplan–Meier curve. Molecularly guided drug repurposing was performed by an integrative connectivity map-prioritized drug-gene network analysis. Results: The immune-oncogenic gene signature consists of 26 mortality-associated immune-related genes. Patients with high-expression signature had a poorer overall survival (log-rank p < 0.001), while gene enrichment analysis revealed cell-cycle checkpoint regulation and inflammatory-immune response signaling pathways affected this high-risk group. The integrative drug-gene network identified eight FDA-approved drugs as promising candidates, including Dasatinib a multi-kinase inhibitor currently investigated for advanced CCA with isocitrate-dehydrogenase mutations. Conclusion: This study proposes the use of the immune-oncogenic gene signature to identify high-risk CCA patients. Future preclinical and clinical studies are required to elucidate the therapeutic efficacy of the molecularly guided drugs as the adjunct therapy, aiming to improve the survival outcome. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Human Cancers)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop