Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
5.2
Journals
Cancers
Special Issues
Cancer Proteometabolomics
share announcement

Cancer Proteometabolomics

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Pathophysiology".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 7485

Special Issue Editors

Prof. Dr. Samir M. Hanash

E-Mail Website
Guest Editor
Department of Clinical Cancer Prevention, Division of Cancer Prevention and Population Sciences, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Interests: cancer biomarkers; proteomics; cancer surfaceome; immunooncology
Special Issues, Collections and Topics in MDPI journals
Dr. Johannes Fahrmann

E-Mail Website
Guest Editor
Department of Clinical Cancer Prevention, MD Anderson Cancer Center, The University of Texas, 1515 Holcombe Blvd., Houston, TX 77030, USA
Interests: cancer biomarkers; metabolomics; metabolic vulnerabilities; therapeutics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of “omic” technologies coupled with advanced statistical approaches (e.g., artificial intelligence) is increasingly being utilized to reveal the molecular drivers of tumorigenesis and to derive novel treatment strategies and biomarkers that inform clinical decision making. While “omic” approaches have largely been advanced in the areas of genomics and transcriptomics, there is considerable interest and merit in exploring proteomics and metabolomics data, as these offer detailed characterization of the functional molecular machinery and the derived biochemical outputs that ultimately effect oncogenesis and shape the tumor microenvironment and tumor–host interaction. Using proteomics, metabolomics, or integrated proteomic and metabolomic approaches in the context of cancer thus has high relevance to the identification of next-generation treatment modalities that target cancer cell dependencies, as well as to biomarkers that inform upon disease status or molecular subtypes.

This Special Issue focuses on the application of proteomics, metabolomics, and integrated proteomics and metabolomics in cancer, with an emphasis on cancer biology, the tumor microenvironment and modulation of the tumor immunophenotype, biomarkers, and novel therapeutic strategies.

We are soliciting original research articles as well as reviews to contribute to this Special Issue.

Prof. Dr. Samir M. Hanash
Dr. Johannes Fahrmann
Guest Editors

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. 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

  • proteomics
  • metabolomics
  • cancer
  • therapeutics
  • biomarkers
  • “omic” integration

Published Papers (3 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

14 pages, 1734 KiB  
Article
Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment
by Kristin J. Lastwika, Wei Wu, Yuzheng Zhang, Ningxin Ma, Mladen Zečević, Sudhakar N. J. Pipavath, Timothy W. Randolph, A. McGarry Houghton, Viswam S. Nair, Paul D. Lampe and Paul E. Kinahan
Cancers 2023, 15(13), 3418; https://doi.org/10.3390/cancers15133418 - 29 Jun 2023
Cited by 1 | Viewed by 1475
Abstract
The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer [...] Read more.
The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody–antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules. Full article
(This article belongs to the Special Issue Cancer Proteometabolomics)
►▼ Show Figures

Figure 1

27 pages, 5746 KiB  
Article
RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma
by Erdal Tasci, Sarisha Jagasia, Ying Zhuge, Mary Sproull, Theresa Cooley Zgela, Megan Mackey, Kevin Camphausen and Andra Valentina Krauze
Cancers 2023, 15(10), 2672; https://doi.org/10.3390/cancers15102672 - 09 May 2023
Cited by 3 | Viewed by 3091
Abstract
Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to [...] Read more.
Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions. However, data acquired using large proteomic panels must be more intuitively interpretable, requiring computational analysis to identify trends. Machine learning is increasingly employed, however, it requires feature selection which has a critical and considerable effect on machine learning problems when applied to large-scale data to reduce the number of parameters, improve generalization, and find essential predictors. In this study, using 7k proteomic data generated from the analysis of serum obtained from 82 patients with GBM pre- and post-completion of concurrent chemoirradiation (CRT), we aimed to select the most discriminative proteomic features that define proteomic alteration that is the result of administering CRT. Thus, we present a novel rank-based feature weighting method (RadWise) to identify relevant proteomic parameters using two popular feature selection methods, least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR). The computational results show that the proposed method yields outstanding results with very few selected proteomic features, with higher accuracy rate performance than methods that do not employ a feature selection process. While the computational method identified several proteomic signals identical to the clinical intuitive (heuristic approach), several heuristically identified proteomic signals were not selected while other novel proteomic biomarkers not selected with the heuristic approach that carry biological prognostic relevance in GBM only emerged with the novel method. The computational results show that the proposed method yields promising results, reducing 7k proteomic data to 7 selected proteomic features with a performance value of 93.921%, comparing favorably with techniques that do not employ feature selection. Full article
(This article belongs to the Special Issue Cancer Proteometabolomics)
►▼ Show Figures

Figure 1

14 pages, 3051 KiB  
Article
Kynureninase Upregulation Is a Prominent Feature of NFR2-Activated Cancers and Is Associated with Tumor Immunosuppression and Poor Prognosis
by Ricardo A. León-Letelier, Ali H. Abdel Sater, Yihui Chen, Soyoung Park, Ranran Wu, Ehsan Irajizad, Jennifer B. Dennison, Hiroyuki Katayama, Jody V. Vykoukal, Samir Hanash, Edwin J. Ostrin and Johannes F. Fahrmann
Cancers 2023, 15(3), 834; https://doi.org/10.3390/cancers15030834 - 29 Jan 2023
Cited by 5 | Viewed by 2545
Abstract
The nuclear factor erythroid 2-related factor 2 (NRF2) pathway is frequently activated in various cancer types. Aberrant activation of NRF2 in cancer is attributed to gain-of-function mutations in the NRF2-encoding gene NFE2L2 or a loss of function of its suppressor, Kelch-like ECH-associated protein [...] Read more.
The nuclear factor erythroid 2-related factor 2 (NRF2) pathway is frequently activated in various cancer types. Aberrant activation of NRF2 in cancer is attributed to gain-of-function mutations in the NRF2-encoding gene NFE2L2 or a loss of function of its suppressor, Kelch-like ECH-associated protein 1 (KEAP1). NRF2 activation exerts pro-tumoral effects in part by altering cancer cell metabolism. Previously, we reported a novel mechanism of NRF2 tumoral immune suppression through the selective upregulation of the tryptophan-metabolizing enzyme kynureninase (KYNU) in lung adenocarcinoma. In the current study, we explored the relevance of NRF2-mediated KYNU upregulation across multiple cancer types. Specifically, using a gene expression dataset for 9801 tumors representing 32 cancer types from The Cancer Genome Atlas (TCGA), we demonstrated that elevated KYNU parallels increased gene-based signatures of NRF2-activation and that elevated tumoral KYNU mRNA expression is strongly associated with an immunosuppressive tumor microenvironment, marked by high expression of gene-based signatures of Tregs as well as the immune checkpoint blockade-related genes CD274 (PDL-1), PDCD1 (PD-1), and CTLA4, regardless of the cancer type. Cox proportional hazard models further revealed that increased tumoral KYNU gene expression was prognostic for poor overall survival in several cancer types, including thymoma, acute myeloid leukemia, low-grade glioma, kidney renal papillary cell carcinoma, stomach adenocarcinoma, and pancreatic ductal adenocarcinoma (PDAC). Using PDAC as a model system, we confirmed that siRNA-mediated knockdown of NRF2 reduced KYNU mRNA expression, whereas activation of NFE2L2 (the coding gene for NRF2) through either small-molecule agonists or siRNA-mediated knockdown of KEAP1 upregulated KYNU in PDAC cells. Metabolomic analyses of the conditioned medium from PDAC cell lines revealed elevated levels of KYNU-derived anthranilate, confirming that KYNU was enzymatically functional. Collectively, our study highlights the activation of the NRF2–KYNU axis as a multi-cancer phenomenon and supports the relevance of tumoral KYNU as a marker of tumor immunosuppression and as a prognostic marker for poor overall survival. Full article
(This article belongs to the Special Issue Cancer Proteometabolomics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop