Morphomolecular Characterization of Neoplastic and Pre-neoplastic Lesions in Solid Tumors

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cell Biology and Pathology".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 778

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Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
Interests: breast cancer; urogenital cancers; neuropathology; molecular pathology; digital pathology; tumor microenvironment
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Department of Experimental and Clinical Medicine, University of Florence, 50121 Florence, Italy
Interests: hormone-sensitive prostate cancer; metastatic castration-resistant prostate cancer; androgen receptor agents; bone health agents; bone metastases; cfDNA; taxanes; biomarkers
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Special Issue Information

Dear Colleagues,

Traditionally, morphological evaluation represented the conventional approach for diagnosing and classifying neoplastic and pre-neoplastic lesions. However, recent advancements in molecular biology techniques have deeply revolutionized our understanding of solid malignancies, contributing to the investigation of genetic alterations, gene expression patterns, epigenetic modifications and protein expression that drive tumor initiation, progression and metastasis. This knowledge has introduced opportunities for the development of targeted therapies that specifically inhibit these pathways. Moreover, molecular profiling integrated with morphological analysis can significantly improve diagnostic accuracy, prognostic stratification, and tailored treatment strategies, leading to improved therapeutic outcomes and minimizing adverse effects.

In conclusion, the morphomolecular characterization of neoplastic and pre-neoplastic lesions in solid malignancies represents a multidisciplinary approach that combines traditional morphological analysis with cutting-edge molecular techniques. This integrated approach provides a comprehensive understanding of the complex landscape of tumors and holds great promise for improving diagnosis, prognosis and treatment outcomes in the field of oncology. Continued research in this area will undoubtedly lead to further advancements and breakthroughs in our fight against cancer.

We especially welcome original translational research studies highlighting new findings in the abovementioned areas, and short communications of preliminary but significant experimental results. In addition, experts in the field will review the current diagnostic, therapeutic and experimental models.

Dr. Giuseppe Nicolò Fanelli
Dr. Edoardo Francini
Guest Editors

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Keywords

  • solid cancer
  • molecular pathology
  • histopathology
  • diagnostic, prognostic and predictive biomarkers

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Published Papers (1 paper)

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Research

17 pages, 6603 KiB  
Article
Multi-Algorithm-Integrated Tertiary Lymphoid Structure Gene Signature for Immune Landscape Characterization and Prognosis in Colorectal Cancer Patients
by Xianqiang Liu, Dingchang Li, Yue Zhang, Hao Liu, Peng Chen, Yingjie Zhao, Guanchao Sun, Wen Zhao and Guanglong Dong
Biomedicines 2024, 12(11), 2644; https://doi.org/10.3390/biomedicines12112644 - 19 Nov 2024
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Abstract
Purpose: Colorectal cancer (CRC) is a common malignancy with a low survival rate as well as a low response rate to immunotherapy. This study aims to develop a risk model based on tertiary lymphoid structure (TLS)-associated gene signatures to enhance predictions of prognosis [...] Read more.
Purpose: Colorectal cancer (CRC) is a common malignancy with a low survival rate as well as a low response rate to immunotherapy. This study aims to develop a risk model based on tertiary lymphoid structure (TLS)-associated gene signatures to enhance predictions of prognosis and immunotherapy response. Methods: TLS-associated gene data were obtained from TCGA-CRC and GEO cohorts. A comprehensive analysis using univariate Cox regression identified TLS-associated genes with significant prognostic implications. Subsequently, multiple algorithms were employed to select the most influential genes, and a stepwise Cox regression model was constructed. The model’s predictive performance was validated using independent datasets (GSE39582, GSE17536, and GSE38832). To further investigate the immune microenvironment, immune cell infiltration in high-risk (HRG) and low-risk (LRG) groups was assessed using the CIBERSORT and ssGSEA algorithms. Additionally, we evaluated the model’s potential to predict immune checkpoint blockade therapy response using data from The Cancer Imaging Archive, the TIDE algorithm, and external immunotherapy cohorts (GSE35640, GSE78200, and PRJEB23709). Immunohistochemistry (IHC) was employed to characterize TLS presence and CCL2 gene expression. Results: A three-gene (CCL2, PDCD1, and ICOS) TLS-associated model was identified as strongly associated with prognosis and demonstrated predictive power for CRC patient outcomes and immunotherapy efficacy. Notably, patients in the low-risk group (LRG) had a higher overall survival rate as well as a higher re-response rate to immunotherapy compared to the high-risk group (HRG). Finally, IHC results confirmed significantly elevated CCL2 expression in the TLS regions. Conclusions: The multi-algorithm-integrated model demonstrated robust performance in predicting patient prognosis and immunotherapy response, offering a novel perspective for assessing immunotherapy efficacy. CCL2 may function as a TLS modulator and holds potential as a therapeutic target in CRC. Full article
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