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Onco, Volume 3, Issue 2 (June 2023) – 4 articles

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2 pages, 374 KiB  
Editorial
Onco: A Promising Player Amidst Oncology Journals
by Constantin N. Baxevanis
Onco 2023, 3(2), 125-126; https://doi.org/10.3390/onco3020009 - 1 Jun 2023
Viewed by 1139
Abstract
As the new Editor-in-Chief of the journal, I believe that I must continue the efforts of my predecessor even more actively and with greater enthusiasm and dedication so that the journal becomes a pole of attraction for the publication of excellent studies of [...] Read more.
As the new Editor-in-Chief of the journal, I believe that I must continue the efforts of my predecessor even more actively and with greater enthusiasm and dedication so that the journal becomes a pole of attraction for the publication of excellent studies of basic, translational and clinical research for the treatment of cancer [...] Full article
29 pages, 1874 KiB  
Review
A Comprehensive Review on the Role of Human Epidermal Growth Factor Receptor 2 (HER2) as a Biomarker in Extra-Mammary and Extra-Gastric Cancers
by Fnu Amisha, Paras Malik, Prachi Saluja, Nitesh Gautam, Tanvi Harishbhai Patel, Arya Mariam Roy, Sunny R. K. Singh and Sindhu Janarthanam Malapati
Onco 2023, 3(2), 96-124; https://doi.org/10.3390/onco3020008 - 26 May 2023
Cited by 5 | Viewed by 3040
Abstract
The human epidermal growth factor receptors (HERs) are expressed abundantly in the human body. The tumorigenic potential of HER2/neu is linked to its overexpression, amplification or somatic mutation. The HER2 gene amplification leading to protein overexpression has been reported in 25–30% of breast [...] Read more.
The human epidermal growth factor receptors (HERs) are expressed abundantly in the human body. The tumorigenic potential of HER2/neu is linked to its overexpression, amplification or somatic mutation. The HER2 gene amplification leading to protein overexpression has been reported in 25–30% of breast cancers and 10–30% of gastric/gastroesophageal cancers. While HER2 is a well-documented predictive, prognostic, and therapeutic marker in breast and gastric/gastroesophageal cancers, its relevance has also been demonstrated in multiple other malignancies. In this article, we will conduct an extensive review of current data pertaining to HER2 amplification, overexpression, or mutation in cancers other than breast and gastric cancers. Full article
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15 pages, 3045 KiB  
Article
Transcriptome Analysis Identifies Tumor Immune Microenvironment Signaling Networks Supporting Metastatic Castration-Resistant Prostate Cancer
by Lawrence P. McKinney, Rajesh Singh, I. King Jordan, Sooryanarayana Varambally, Eric B. Dammer and James W. Lillard, Jr.
Onco 2023, 3(2), 81-95; https://doi.org/10.3390/onco3020007 - 10 Apr 2023
Viewed by 2881
Abstract
Prostate cancer (PCa) is the second most common cause of cancer death in American men. Metastatic castration-resistant prostate cancer (mCRPC) is the most lethal form of PCa and preferentially metastasizes to the bones through incompletely understood molecular mechanisms. Herein, we processed RNA sequencing [...] Read more.
Prostate cancer (PCa) is the second most common cause of cancer death in American men. Metastatic castration-resistant prostate cancer (mCRPC) is the most lethal form of PCa and preferentially metastasizes to the bones through incompletely understood molecular mechanisms. Herein, we processed RNA sequencing data from patients with mCRPC (n = 60) and identified 14 gene clusters (modules) highly correlated with mCRPC bone metastasis. We used a novel combination of weighted gene co-expression network analysis (WGCNA) and upstream regulator and gene ontology analyses of clinically annotated transcriptomes to identify the genes. The cyan module (M14) had the strongest positive correlation (0.81, p = 4 × 10−15) with mCRPC bone metastasis. It was associated with two significant biological pathways through KEGG enrichment analysis (parathyroid hormone synthesis, secretion, and action and protein digestion and absorption). In particular, we identified 10 hub genes (ALPL, PHEX, RUNX2, ENPP1, PHOSPHO1, PTH1R, COL11A1, COL24A1, COL22A1, and COL13A1) using cytoHubba of Cytoscape. We also found high gene expression for collagen formation, degradation, absorption, cell-signaling peptides, and bone regulation processes through Gene Ontology (GO) enrichment analysis. Full article
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16 pages, 2272 KiB  
Article
CT Radiomics and Clinical Feature Model to Predict Lymph Node Metastases in Early-Stage Testicular Cancer
by Catharina Silvia Lisson, Sabitha Manoj, Daniel Wolf, Jasper Schrader, Stefan Andreas Schmidt, Meinrad Beer, Michael Goetz, Friedemann Zengerling and Christoph Gerhard Sebastian Lisson
Onco 2023, 3(2), 65-80; https://doi.org/10.3390/onco3020006 - 10 Apr 2023
Cited by 2 | Viewed by 3018
Abstract
Accurate retroperitoneal lymph node metastasis (LNM) prediction in early-stage testicular germ cell tumours (TGCTs) harbours the potential to significantly reduce over- or undertreatment and treatment-related morbidity in this group of young patients as an important survivorship imperative. We investigated the role of computed [...] Read more.
Accurate retroperitoneal lymph node metastasis (LNM) prediction in early-stage testicular germ cell tumours (TGCTs) harbours the potential to significantly reduce over- or undertreatment and treatment-related morbidity in this group of young patients as an important survivorship imperative. We investigated the role of computed tomography (CT) radiomics models integrating clinical predictors for the individualised prediction of LNM in early-stage TGCT. Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. Dedicated radiomics software was used to segment 273 retroperitoneal lymph nodes and extract features. After feature selection, radiomics-based machine learning models were developed to predict LN metastasis. The robustness of the procedure was controlled by 10-fold cross-validation. Using multivariable logistic regression modelling, we developed three prediction models: a radiomics-only model, a clinical-only model, and a combined radiomics–clinical model. The models’ performances were evaluated using the area under the receiver operating characteristic curve (AUC). Finally, decision curve analysis was performed to estimate the clinical usefulness of the predictive model. The radiomics-only model for predicting lymph node metastasis reached a greater discrimination power than the clinical-only model, with an AUC of 0.87 (±0.04; 95% CI) vs. 0.75 (±0.08; 95% CI) in our study cohort. The combined model integrating clinical risk factors and selected radiomics features outperformed the clinical-only and the radiomics-only prediction models, and showed good discrimination with an area under the curve of 0.89 (±0.03; 95% CI). The decision curve analysis demonstrated the clinical usefulness of our proposed combined model. The presented combined CT-based radiomics–clinical model represents an exciting non-invasive tool for individualised LN metastasis prediction in testicular germ cell tumours. Multi-centre validation is required to generate high-quality evidence for its clinical application. Full article
(This article belongs to the Topic Artificial Intelligence in Cancer, Biology and Oncology)
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