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Roccuzzo G, Bongiovanni E, Tonella L, Pala V, Marchisio S, Ricci A, Senetta R, Bertero L, Ribero S, Berrino E, Marchiò C, Sapino A, Quaglino P, Cassoni P. Emerging prognostic biomarkers in advanced cutaneous melanoma: a literature update. Expert Rev Mol Diagn 2024; 24:49-66. [PMID: 38334382 DOI: 10.1080/14737159.2024.2314574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 02/01/2024] [Indexed: 02/10/2024]
Abstract
INTRODUCTION Over the past two years, the scientific community has witnessed an exponential growth in research focused on identifying prognostic biomarkers for melanoma, both in pre-clinical and clinical settings. This surge in studies reflects the need of developing effective prognostic indicators in the field of melanoma. AREAS COVERED The aim of this work is to review the scientific literature on the most recent findings on the development or validation of prognostic biomarkers in melanoma, in the attempt of providing both clinicians and researchers with an updated broad synopsis of prognostic biomarkers in cutaneous melanoma. EXPERT OPINION While the field of prognostic biomarkers in melanoma appears promising, there are several complexities and limitations to address. The interdependence of clinical, histological, and molecular features requires accurate classification of different biomarker families. Correlation does not imply causation, and adjustments for confounding factors are often overlooked. In this scenario, large-scale studies based on high-quality clinical trial data can provide more reliable evidence. It is essential to avoid oversimplification by focusing on a single biomarker, as the interactions among multiple factors contribute to define the disease course and patient's outcome. Furthermore, implementing well-supported evidence in real-life settings can help advance prognostic biomarker research in melanoma.
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Affiliation(s)
- Gabriele Roccuzzo
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Eleonora Bongiovanni
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Luca Tonella
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Valentina Pala
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Sara Marchisio
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Alessia Ricci
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Rebecca Senetta
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Luca Bertero
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Simone Ribero
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Enrico Berrino
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Caterina Marchiò
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Anna Sapino
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Pietro Quaglino
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Paola Cassoni
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
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Abbasi EY, Deng Z, Magsi AH, Ali Q, Kumar K, Zubedi A. Optimizing Skin Cancer Survival Prediction with Ensemble Techniques. Bioengineering (Basel) 2023; 11:43. [PMID: 38247920 PMCID: PMC10813432 DOI: 10.3390/bioengineering11010043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
The advancement in cancer research using high throughput technology and artificial intelligence (AI) is gaining momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This study focuses on predicting skin cancer and analyzing overall survival probability. We employ the Kaplan-Meier estimator and Cox proportional hazards regression model, utilizing high-throughput machine learning (ML)-based ensemble methods. Our proposed ML-based ensemble techniques are applied to a publicly available dataset from the ICGC Data Portal, specifically targeting skin cutaneous melanoma cancers (SKCM). We used eight baseline classifiers, namely, random forest (RF), decision tree (DT), gradient boosting (GB), AdaBoost, Gaussian naïve Bayes (GNB), extra tree (ET), logistic regression (LR), and light gradient boosting machine (Light GBM or LGBM). The study evaluated the performance of the proposed ensemble methods and survival analysis on SKCM. The proposed methods demonstrated promising results, outperforming other algorithms and models in terms of accuracy compared to traditional methods. Specifically, the RF classifier exhibited outstanding precision results. Additionally, four different ensemble methods (stacking, bagging, boosting, and voting) were created and trained to achieve optimal results. The performance was evaluated and interpreted using accuracy, precision, recall, F1 score, confusion matrix, and ROC curves, where the voting method achieved a promising accuracy of 99%. On the other hand, the RF classifier achieved an outstanding accuracy of 99%, which exhibits the best performance. We compared our proposed study with the existing state-of-the-art techniques and found significant improvements in several key aspects. Our approach not only demonstrated superior performance in terms of accuracy but also showcased remarkable efficiency. Thus, this research work contributes to diagnosing SKCM with high accuracy.
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Affiliation(s)
- Erum Yousef Abbasi
- State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
| | - Zhongliang Deng
- State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
| | - Arif Hussain Magsi
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;
| | - Qasim Ali
- Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Pakistan;
| | - Kamlesh Kumar
- School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
| | - Asma Zubedi
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China;
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Deng H, Chen Y, An R, Wang J. Pyroptosis-related lncRNA prognostic signatures for cutaneous melanoma and tumor microenvironment status. Epigenomics 2023; 15:657-675. [PMID: 37577979 DOI: 10.2217/epi-2023-0139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023] Open
Abstract
Aims: To explore whether the expression of pyroptosis-related lncRNAs makes a difference in the prognosis and antitumor immunity of cutaneous melanoma (CM) patients. Methods: A series of analyses were conducted to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to further assess the associations among immune status, tumor microenvironment and the prognostic risk model. Results: Eight pyroptosis-related lncRNAs relevant to prognosis were ascertained and applied to establish the prognostic risk model. The low-risk group had a higher overall survival rate. Conclusion: The established prognostic risk model presents better prediction ability for the prognosis of CM patients and provides new possible therapeutic targets for CM.
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Affiliation(s)
- Huiling Deng
- Department of Plastic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
| | - Yuxuan Chen
- Department of Plastic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
| | - Ran An
- Department of Plastic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
| | - Jiecong Wang
- Department of Plastic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
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Bukhari I, Zhang Y, Thorne RF, Mi Y. Editorial: Complexity of tumor microenvironment: A major culprit in cancer development, volume II. Front Endocrinol (Lausanne) 2022; 13:1126778. [PMID: 36714569 PMCID: PMC9878851 DOI: 10.3389/fendo.2022.1126778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 12/27/2022] [Indexed: 01/15/2023] Open
Affiliation(s)
- Ihtisham Bukhari
- Henan Key Laboratory of Helicobacter pylori, Microbiota and Gastrointestinal Cancers, Marshall Medical Research Center, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Translational Research Institute, Henan Provincial and Zhengzhou City Key Laboratory of Non-coding RNA and Cancer Metabolism, Henan International Join Laboratory of Non-coding RNA and Metabolism in Cancer, Henan Provincial People’s Hospital, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Yuanwei Zhang
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, China
| | - Rick Francis Thorne
- Translational Research Institute, Henan Provincial and Zhengzhou City Key Laboratory of Non-coding RNA and Cancer Metabolism, Henan International Join Laboratory of Non-coding RNA and Metabolism in Cancer, Henan Provincial People’s Hospital, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China
- School of Environmental and Life Sciences, University of Newcastle, Callaghan, NSW, Australia
- *Correspondence: Rick Francis Thorne, ; Yang Mi,
| | - Yang Mi
- Henan Key Laboratory of Helicobacter pylori, Microbiota and Gastrointestinal Cancers, Marshall Medical Research Center, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Rick Francis Thorne, ; Yang Mi,
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