We are pleased to invite you to read the most viewed papers in 2023 in Cancers (ISSN: 2072-6694) on the topic of cancer informatics and big data. The paper list is as follows:

“Bioinformatics Screen Reveals Gli-Mediated Hedgehog Signaling as an Associated Pathway to Poor Immune Infiltration of Dedifferentiated Liposarcoma”
by Erik P. Beadle, Natalie E. Bennett and Julie A. Rhoades
Cancers 2023, 15(13), 3360; https://doi.org/10.3390/cancers15133360
Available online: https://www.mdpi.com/2072-6694/15/13/3360

“Serum Mass Spectrometry Proteomics and Protein Set Identification in Response to FOLFOX-4 in Drug-Resistant Ovarian Carcinoma”
by Domenico D’Arca, Leda Severi, Stefania Ferrari, Luca Dozza, Gaetano Marverti, Fulvio Magni, Clizia Chinello, Lisa Pagani, Lorenzo Tagliazucchi, Marco Villani et al.
Cancers 2023, 15(2), 412; https://doi.org/10.3390/cancers15020412
Available online: https://www.mdpi.com/2072-6694/15/2/412

“Prospects of POLD1 in Human Cancers: A Review”
by Michał Gola, Przemysław Stefaniak, Janusz Godlewski, Barbara Alicja Jereczek-Fossa and Anna Starzyńska
Cancers 2023, 15(6), 1905; https://doi.org/10.3390/cancers15061905
Available online: https://www.mdpi.com/2072-6694/15/6/1905

“Multi-Omic Analysis of CIC’s Functional Networks Reveals Novel Interaction Partners and a Potential Role in Mitotic Fidelity”
by Yuka Takemon, Véronique G. LeBlanc, Jungeun Song, Susanna Y. Chan, Stephen Dongsoo Lee, Diane L. Trinh, Shiekh Tanveer Ahmad, William R. Brothers, Richard D. Corbett and Alessia Gagliardi et al.
Cancers 2023, 15(10), 2805; https://doi.org/10.3390/cancers15102805
Available online: https://www.mdpi.com/2072-6694/15/10/2805

“Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications”
by Andrew Patterson, Abdurrahman Elbasir, Bin Tian and Noam Auslander
Cancers 2023, 15(7), 1958; https://doi.org/10.3390/cancers15071958
Available online: https://www.mdpi.com/2072-6694/15/7/1958

“Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review”
by Wilson Ong, Lei Zhu, Yi Liang Tan, Ee Chin Teo, Jiong Hao Tan, Naresh Kumar, Balamurugan A. Vellayappan, Beng Chin Ooi, Swee Tian Quek and Andrew Makmur et al.
Cancers 2023, 15(6), 1837; https://doi.org/10.3390/cancers15061837
Available online: https://www.mdpi.com/2072-6694/15/6/1837

“Impact of Stain Normalization on Pathologist Assessment of Prostate Cancer: A Comparative Study”
by Massimo Salvi, Alessandro Caputo, Davide Balmativola, Manuela Scotto, Orazio Pennisi, Nicola Michielli, Alessandro Mogetta, Filippo Molinari and Filippo Fraggetta
Cancers 2023, 15(5), 1503; https://doi.org/10.3390/cancers15051503
Available online: https://www.mdpi.com/2072-6694/15/5/1503

“Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging”
by Marina Z. Joel, Arman Avesta, Daniel X. Yang, Jian-Ge Zhou, Antonio Omuro, Roy S. Herbst,Harlan M. Krumholz and Sanjay Aneja
Cancers 2023, 15(5), 1548; https://doi.org/10.3390/cancers15051548
Available online: https://www.mdpi.com/2072-6694/15/5/1548

“Involvement of Epithelial–Mesenchymal Transition Genes in Small Cell Lung Cancer Phenotypic Plasticity”
by Sarah M. Groves, Nicholas Panchy, Darren R. Tyson, Leonard A. Harris, Vito Quaranta and Tian Hongn
Cancers 2023, 15(5), 1477; https://doi.org/10.3390/cancers15051477
Available online: https://www.mdpi.com/2072-6694/15/5/1477

“Biomarkers of Tumor Heterogeneity in Glioblastoma Multiforme Cohort of TCGA”
by Garrett Winkelmaier, Brandon Koch, Skylar Bogardus, Alexander D. Borowsky and Bahram Parvi
Cancers 2023, 15(8), 2387; https://doi.org/10.3390/cancers15082387
Available online: https://www.mdpi.com/2072-6694/15/8/2387

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Cancers Editorial Office